I am Satish Pariani, working as assistant professor in Department of Mechanical, Vulture Institute of Technology, Sholabu. Now we are discussing the problems faced by entrepreneurs. At the end of this session, students will be able to understand the problems and their solutions, what the entrepreneurs face. The contents. Here majorly we will discuss on the task delegation, choosing what to sell and the marketing strategy problems associated with entrepreneurs and their solutions. Entrepreneurs face major challenges in today's ultra-competitive business world, fortunately, contemporary times have also blessed entrepreneurs with more resources for tackling those problems than ever before. The problems can be from technical world, marketing world, human world or financial world. Now we are discussing the fourth problem that is called task delegation. The challenge the entrepreneur face, you know, you need to delegate or outsource task, but it seems every time you do something gets messed up and you have to redo it anyway. As an entrepreneur, as a new startup, generally these are the problems, when there is a new startup, when the new startup or new organization which has been evolving, task delegation is the biggest problem. Whether I should do all in considering manufacturing activities, considering manufacturing, if I am doing in the manufacturing world, whether all I should do in house under one roof or whether I go for outsourcing, certain tasks which I need to delegate to my suppliers or to some other outsourcing agencies, whether HR training should I take it or whether I should go for outsourcing. Whether I need to check the bills, each and every small bills which marketing people have spent it or whether I give it to account department. We need to think on these kind of examples. Generally if we do these kind of day to day work, our organization cannot grow to that extent. We need to set up certain processes. We need to set up certain organization, people who will be getting done this kind of work and we need to concentrate only on the basic strategic thinking to grow our organization. Big decisions we need to go. But once we are engaged in small, small things, our minds are shifting in that we get messed up and we are with so many to do list. Now think it, the problem of time management to certain extent, whether it can be solved with task delegation, think the answer to this is yes. Once we start delegating our task, our time management issues will also getting resolved. Reason being that we are into now not day to day activities, we are into some strategic activities. We are not in day to day operations. We are into certain big operation, big thinking to certain time management issues can be solved. Now solution of task delegation, find good employees and good outsource contract help. As I told you, general training is required. As an employee, I need certain training to get my skills groom up. If I get the same training in house, also one time, two times, three times, I start feeling bored, boredom. So these tasks, this work contract should be done from outsource agencies. Many times the good employees are also available, which can provide a good training. But yes, you need to make a feel that yes, we are getting trained. You might have to pay a little more for it, but it's saving time and resulting earning potential more than makeup for it. Once employee morale is boosted, it helps not only to improve its productivity, but its productivity of organization also. He starts thinking in some different way and that extra cost for training, let us say, outsource training what we have done can help a lot to get more big profits and make up for the small training costs associated. This is example of outsource training for HR employees. The same task delegation, once we start in house, suppose I have taken in previous slide the example of suppose we start looking at small, small bills of marketing people. So much time is wasted to cross check everything. Suppose this I give to my financial department, whom I trust to find a good employee to give him that work. What will happen? I will save time. My role will be only to signing and finalizing, yes, whether to pass that bill or not. Suppose for that one extra employee also I need to keep it up. So much time I will get it, that employee is helping me to get so much time for which I have that time I will utilize for taking some static decisions for the benefit of organization and that is far, far more I can say comparatively by keeping one employee. So these are few examples from both ways. For in manufacturing organization, a financial bill, outsource, HR, outsource same in day to day we need to take certain action and we need to delegate the task. This is the biggest problem which these days are faced by generally startup employees. The old big employers already know the importance of this and they start delegating but many times also whether to do it or not do it they are into this. So these are the problems and probably this is a solution, a simple solutions which will help us to take a better decisions and take a better profits. The next big ultra specific is what you want to, what you want done. It will take a little more time at first but write down the detail step listing exactly what you want your help to do. Don't make assumptions and don't assume your help will be able to think of themselves. Now after discussing the star solution the next problem for the entrepreneurs what to sell. The challenge is you know you can make a mint if you just knew what products and services to sell. You have the financial aid, you have the financial support but what you want to sell it. You are just unaware how to pick a niche whether you want to sell a product which is selling at a high profit but a low volume or whether you want to make a product which is selling at a low profit but higher volume. You need to make a decision on what you want to sell and how you are building your organization in that way. It is decision of a volume game and generally low profit high volume are the companies which are generally quite ahead. We need because you cannot find a product in the market unless you have a you can sell it unless there is some specialty some niche kind of thing in it whether then you have to be innovative in your product completely new product and which takes a lot of time and which also takes a lot of time market to pick up even your product is good but yes how to do so. I will tell you one example, gold and high technology was developed by LG Electronics. It was developed by certain other organization also. So who sold it? It was LG Electronics who made a great profits. What to sell? We are selling certain product or we are selling certain feature also is does matter. The solution is admit that you are weak in the added five prospector niches and delegate the task to someone who is strong in this area. You do not have to hire a huge expensive marketing firm rather recruit a freelancer which has experience in whatever type of field you are considering entering retail e-commerce service industry publishing have conduct market research and create a report with suggested niches and potential profit margins and completes what analysis the strength, weaknesses and operation threats everything are displayed. We need to be very clear in this. The next is marketing strategy. The challenge is you do not know the best way to market your products and services print online mobile advertising access you want to maximize the return on investment with efficient targeting marketing that gets resulted and the solution is the solution again if you are not adept at creating marketing plans and placing ads it is good idea to outsource your marketing strategy to someone who is at this point all you need to is core marketing plan what marketing activities will undertake to motivate purchases give your planner a budget and tell them a craft a plan that officially utilize their budget to product produce profits again whether you will do for paper marketing newspaper marketing we can say or whether you can afford for radios you go for amazons or some e-retailers or some TV ads be very clear which target customers you are utilizing the references which are again use are from NPTEL 10 challenges faced by entrepreneurs by deluxe.com and the book utilizes project management and control by Narendra Singh Himalaya Publication House thank you.


I saw my buddy Chef Bob make this awesome breakfast recipe, so let's give it a try. Take our sausage patty and we press it down and around in our muffin tin. Now we're going to crack us some eggs. I'm going to say six eggs for eight muffin tins. Whip up your eggs and evenly distribute a little bit of onion to each cup, some black olives because I love them, and some red bell pepper.  That's the red top. Sprinkle generously right on top. Now we're going to our 400 degree oven. We'll see you in 20. Let's get a look. Oh, it's looking fantastic. Now it's time to get a bite. As old Chef Bob would like to say, bam, money. Wow, 10 out of 10 and I do it again. Yum, yum, get yourself.


Depending on who you ask, being quiet can be both a blessing and a curse. There's nothing wrong with being quiet, but there are often certain stereotypes or assumptions people make that might not be entirely true. Not all quiet people are introverts, but they share similar traits and behaviors. So whether you feel like you know frighteningly little about your quiet friend or are a quiet person and want to know more, here are five psychological facts about quiet people. If you need to be heard, talk to a quiet friend. When opening up to someone, you probably don't want that person to make it all about themselves or interrupt and make assumptions. The ideal listener is someone who can calmly hear you, collect their thoughts, and only give advice when it's wanted or needed. There's a lot that goes into being a good listener, but quiet people have a head start naturally preferring to be quite longer. A 2020 study published in the International Journal of Listening found that empathetic listening improved interpersonal relationships greatly. The study teaches us that when someone uses empathetic listening, it boosts our satisfaction and relational quality. Basically putting the time into listening to someone properly will reduce the chance of conflicts coming up later. Anyone can empathetically listen, but some might find it more difficult. When trying to listen, we tend to want to offer solutions and steer the conversation's direction. The problem is that the person who should be listening is trying to control the conversation. There should be two roles, a speaker and a listener. Quiet people are able to put aside their thoughts and often accept the role of the listener for as long as they have to. Quiet people as leaders. There are definitely introverted leaders out there. Film directors, CEOs, and retail managers can all be successful leaders while being introverts.  So how do they manage to lead despite seemingly not having ideal extroverted traits for leadership? It comes down to how they interact with the people they lead. As author Carmen Nobel writes, both types of leaders, the extroverts and the introverts can be equally successful or ineffectual, but with different groups of employees. An extroverted leader will take the reins and feel comfortable with telling people exactly what is required of them and delegating. An introverted leader is far more effective when working with passionate team members who have their own ideas. An introverted leader is able to understand and fully utilize their strengths and their team strengths. This is why they're so effective in creative fields like film. They're able to get the best out of their team's skills. Being quiet doesn't equal depressed. It's true that introverts and quiet or reclusive people are more likely to be depressed. The idea of extroverts being extremely happy and introverts being less happy is not entirely false but might be a little exaggerated. In the Handbook of Solitude there is a chapter on introversion, solitude, and subjective well-being that examines the differences between introverts and extroverts with regards to happiness. Extroverted people can try to cope with their depression in ways that suit them such as going out often and constantly being busy with people. On the other hand, introverts might show more stereotypical signs of depression just by being themselves. Extroverts do tend to be more happy but according to the Handbook of Solitude that also depends on how you measure happiness. Being quiet doesn't make you more creative or intelligent but neither does being loud. The relationship between personality traits, creativity, and intelligence is a subject that has been investigated quite thoroughly. If we imagine the kinds of people we think of as intelligent in movies or video games you might think of someone who is quiet or reserved, someone who is always deep in thought and away from the crowd. While this sort of archetype definitely can exist in real life, it probably isn't the majority. It should be said that being quiet or being an introvert doesn't inherently make you more intelligent or more creative. In her 2007 dissertation, psychologist Talana Nade found that there is a relationship between someone's creative ability and their intelligence. The traits found in a person that is more creative and intelligent were the opposite of what you'd expect from the stereotypes in movies. They found that risk taking, playfulness, sense of humor, openness to new experiences, freedom, flexibility, and originality were all common amongst the more creative and intelligent individuals. But these traits don't necessarily describe a loud person. They might be more outgoing and have all of these traits and still be quiet. Things such as confidence and positive self-image seem to correlate with greater creativity. Other studies suggest that there is absolutely no correlation between creativity and personality traits, so take it all with a grain of salt. They are more self-aware, for better or worse. To get a quiet person to talk can be a bit of a tall order. Sometimes quiet people stay quiet just because of their self-awareness. When we're in a casual social setting, our focus should be on projecting outwards and engaging with the people around us. Self-awareness is a good thing. It keeps you grounded, but it comes with downsides. Do you ever feel like you can't fully appreciate a situation because you're too self-critical or over-analyzing? At parties, everyone else seems to be lost in conversation, having fun and generally letting loose.  This can be fun to know that you're seeing the bigger picture, but it can also feel alienating. Behavior specialist Elaine Moran and James Everett explain that whether your self-awareness is helpful or harmful depends on your inner speech or the way that you think. If your thoughts are cold and harsh and you feel like a recluse in social situations, then maybe your self-awareness is taking out all the fun when you're social. But if you enjoy being quiet around your friends and feel at peace while simply existing in the same space, then your self-awareness is enhancing a situation that, as an introvert, you might not get a lot of fun out of. So, what did we learn about quiet people? You can trust them to listen and they can be capable leaders. They aren't quiet because they're depressed. They aren't more intelligent just because they're quiet, but they are more self-aware. Most importantly, don't underestimate quiet people. If you are a quiet person, let us know if you relate to any of these. And if you have a quiet friend, share this video with them. Before you go, remember to leave a like and subscribe to Psych2Go for more videos like these.


Hello, Slush. So good to be here. My name is Sam Sihbonan. I'm one of the founders and the CEO of Solo, and we're preventing epidemics with genomics. I was in medical school during COVID, and I realized that in order for us to prevent the next possible pandemic, new tools would be necessary. And about a year ago when I dropped out and met my co-founder, Timon Kerko, we were thinking of the risk that infectious diseases pose to us in the future. It's been estimated that there's a 27% chance of a COVID-like pandemic in the future, which is a frightening thought. And with the rise of antibiotic resistance, superbugs are getting more and more common. These are bugs that no longer respond to normal treatments. And in the U.S., if you are admitted in the hospital, you have a 5% chance of getting infected during your stay. And these trends pose a serious threat to our modern way of lives, our economy, and of course to us as individuals. But as ambitious founders, we wanted to figure out a solution, do the math. That's why we started Solo. Our answer for this was to prevent these infections from happening by building a genomic infrastructure that would help the hospital epidemiologists and doctors understand the infections happening on a genomic level, on a first principles level. There's a technological shift happening in infectious diseases. DNA sequencing is becoming more affordable and approachable. Post-COVID, almost every single laboratory in the U.S. in a hospital has acquired DNA sequencing device. And genomic surveillance and prevention has been shown to be by far the most efficient way to prevent these infections from spreading. There's also been a 10x growth in data in the last seven years. But at the same time, it takes about 14 days for the hospitals to take from sample to analysis of the results, which is way too slow to actually get any meaningful results. Putting all this together, we realized that a product or service that would combine both the speed of analysis, ease of use so you wouldn't need to buy from petition anymore and also reliability in the results and also having a strong foothold in science. And that's why we built the Solo platform to help the epidemiologists inside of hospitals and also public health level to understand the change of transmission that are causing the infections. Let us show you how the product can actually help in real life. So, Meet Mark, he's an epidemiologist working in a Pennsylvania hospital, comes in the morning, opens his laptop and logs into the Solo platform. He sees that there's a new sample, genome sample of a bacteria added to the platform, clicks the results open and sees that a patient that has been in a hospital ward has been diagnosed with a bacteria that is high risk in both virulence and resistance and therefore can cause an infection. And looking at the past genomes in the platform, he can figure out what caused this infection. And in this case, it's a patient in the same hospital room as the patient under inspection. And with this information, Mark is able to advise the hospital staff to contain the infection, save lives and money for the hospital. Let's look at another example. So, this time Elise, she's a medical director at a public health agency in the US. And they're also looking at the Solo platform and they're investigating this nasty outbreak of bacteria in Pennsylvania and nearby states. And looking at the platform's map view, they can see the spread of infection and also the original hospital that was the cause of infection. They can see Mark's map, a hospital popping up on the map. And with this information, Elise is actually able to contact Mark, share all their learnings and their understanding of how the spread of disease has actually happened. And with this collaboration, they're able to prevent this small epidemic that is limited to one state or a few states from becoming a nationwide disaster. We've been working with some amazing people, for example, at Stanford and the CDC. And the feedback we've gotten from the platform is amazement in how fast and easy we've made the analysis of samples. Our goal with Solo is really to build this kind of a first real-time bacterial database to see how the bacteria are evolving over time. We want to produce a 10x ROI for the hospitals that we work with. Right now, they're losing tens of millions dollars annually. We want to save one million lives and become this leading database of genomes to enable better treatments in the future. Or in other words, we want to become the pioneers of preventative and proactive epidemiology. And why pioneers? It's because we're not building an entirely new technology. We're really building the railroads or the electric wires of the world of bacterial genomics. So what's next? We'll be continuing to work with our amazing partners. We've developed a service model that can take the 14 days down to three days in analysis speed. And also, we're looking for our first US hospital pilot right now. We raised our pre-seed round from Lifeline Ventures and Wave Ventures in January. And yeah, I think with the roadmap we're having currently, we see a pathway to getting our first 100 million dollars in revenue in the next about five to six years. And that's of course only the beginning. Oh, this QR code has shifted. Nice. So yeah, thank you so much. Feel free to contact me through the QR code or come talk to me at the event here. Hope you have an amazing slush. Have fun at the after-party. And thank you so much for this. Bye.


Today we're gathered in formal convocation to celebrate the 240th commencement exercises at Purdue University. Please rise and join in singing our national anthem and then remain standing for the invocation offered today by Father Patrick Bacowskis, Pastor and Director of Campus Ministry, St. Thomas Aquinas. There was a woman who died not so very long ago who has inspired millions of people by her life's example and her encouragement for all of us to be attentive to a higher, more purposeful calling. In the places where Mother Teresa of Calcutta lived and worked, she would have these words placed on the walls. And as you go forth today from Purdue, may this reflection she had in front of her every day inspire you as well. People are often unreasonable, illogical, and self-centered. Forgive them anyway. If you are kind, people may accuse you of selfish, ulterior motives. Be kind anyway. If you are successful, you will win some false friends and some true enemies. Succeed anyway. If you are honest and frank, people may cheat you. Be honest and frank anyway. What you spend years building someone could destroy overnight. Build anyway. If you find serenity and happiness, they may be jealous. Be happy anyway. The good you do today, people will often forget. Do good anyway. Give the world the best you have, and it may never be enough. Give the world the best you've got anyway. You see, in the final analysis, it is between you and your God. It was never between you and them anyway. This is the moment. This is the time. This is the day. I never expected this. To be addressing you with me in an empty hall and you far away. Wherever you're watching this virtual ceremony, I hope you're surrounded by people you love, people who helped you reach this moment of achievement. As is my practice, I wrote a commencement speech over last Christmas at a time when COVID-19 had only recently gotten its name. I chose a theme without any clue what was coming. In a different year, I might have felt obliged to start over, but as things have unfolded, the events of recent months have in some ways made my chosen topic seem at least as relevant as the day I wrote it. So here goes. Welcome, graduates and friends, to this day of celebration. Those of you in the caps and gowns are gathered to celebrate the great accomplishment that is a degree from Purdue University. Meanwhile, your parents are, I know, quietly celebrating the clearing of the final tuition check. Congratulations to you all. Purdue celebrated its own landmark this year, our 150th anniversary. Since it coincided with the 50th anniversary of the moon landing by our most famous alum, Neil Armstrong's stories were abundant. My favorite claims that later in life, Commander Armstrong took to telling corny, lame jokes about the moon. And when nobody laughed, he'd say, well, I guess you had to be there. A year or so ago, a major national journalist visited our campus and later wrote a gracious complimentary article about what he saw here. While I enjoyed his accounts of the progress and successful results he thought he'd witnessed, my favorite part of the column was a single phrase, basically a throwaway line. He described Purdue as a happy place. It got me wondering how many college campuses these days would strike a visitor quite that way. I hope it's been that kind of place for you. We know you've worked hard and fought through a lot of pressure. You'll probably remember that. Just wait for those scary dreams where you haven't studied for the test or can't find the exam room. But I hope that among your memories of these years, I was happy there, is prominent among them. I've reflected on that more and more during your last couple years with us. Your parents love you and are proud of you, but from time to time they also must worry about you. That's what we parents do. I hope it's okay if I worry about you and your futures a little also. I've sometimes used these commencements to fret out loud about trends that trouble me in that big, wide world you're about to enter. In recent years, I've spoken about the tribalism that now divides Americans. I've talked about the seeming shortage of emotional resilience and grit in your peer group. Twice, I've found myself urging graduates to guard against the so-called big sort, the tendency for young people of your quality and educational attainment to cluster together professionally and socially and to drift apart from those of different backgrounds. But one thing I never expected to worry about, but now do a little, is you being lonely. I have known you and met thousands of you personally in an environment that, despite our size, does a pretty good job of getting people together, creating bonds among them. A thousand clubs, dozens of faith-based organizations, our Greek system, and maybe our best examples of true communities are co-op residential houses where students not only live but cook, clean, and do repairs together, and most recently the learning communities where thousands of boiler makers live in mutual support with others who are studying the same subject matter. Elsewhere, the academic journals and lay periodicals are now filled with research about the, quote, epidemic of loneliness, quote, in our society. Surveys report record numbers of Americans living alone and suffering from strong feelings of isolation. Many view it as a new public health crisis linked to rising rates of depression, anxiety, even suicide. A lack of strong social relationships has been found to raise the risk of premature death by as much as 50 percent. It's as bad for you as smoking 15 cigarettes a day. It's not just an American phenomenon. The government of Britain has appointed a minister for loneliness, and all this was before anyone heard of COVID-19. Most startling and alarming are studies finding that the worst loneliness today is not among adults or the elderly, but in your age group. Astonishing percentages of today's young people say they have few, if any, close friends. Dating and other traditional forms of youthful interaction have declined sharply. The University of Southern California recently named a director of belonging. And while the title may be unique, I'm sure the idea is not. We humans are social creatures, or as sometimes said, relational beings. The ability to communicate, interact, and collaborate is what defines us and what assured our evolution as the dominant, civilizing species on this planet. We thrive on contact with others. We suffer severely from its absence. The worst punishment we impose on a heinous criminal is solitary confinement. Much of our literature and religious tradition thinks of hell as a state of total permanent aloneness. So an outbreak of loneliness is worth worrying about, especially where those with the longest to live are a big part of it. In the case of your elders, sociologists can identify some obvious causes. Plummeting birth rates play a role. Having fewer children and fewer siblings limits the opportunity for caring contact. In a short two decades, the percentage of retirement age citizens living within 10 miles of their children in the same neighborhood with any relative, or having a good friend living nearby dropped by double digits. It's easy to see how that leads to greater loneliness. But particularly in the case of your age cohort, there is no doubt that the sudden eruption and dominance of what we call social media, I often think anti-social media would be a better term, has played a huge role. Some scholars put all the blame there. One major article was titled, Have Smartphones Destroyed a Generation? It wasn't supposed to be this way. The original promise of all the Facebooks and Twitters was that they would connect us in wonderful new ways. But connection over a text message or what is often a glamorized presentation of one's daily life just isn't the same as in-person contact. In fact, it often turns out to separate and alienate its users more than it brings them together. I remain concerned that as members of the new knowledge aristocracy, absent a little special effort, you will rarely make friends different from yourselves. Now, studying the growing evidence about isolation, I'm concerned that you, the first age group raised entirely in the iPhone era, won't make many friends at all. In my own college days, a briefly famous Harvard professor offered probably the worst advice ever given to a younger generation. Encouraging both drug use and a non-productive lifestyle, Dr. Timothy Leary suggested, quote, turn on, tune in, drop out. Lately, I've been thinking the best advice one could give you, tomorrow's leaders, might be the exact opposite. Turn off, tune out, drop in. As in, turn off the phone more often, tune out the video screen, drop in personally on friends old and new. For most of human history, personal contact was hard to avoid. Suddenly, our digital age can mean it requires extra effort. Confession being good for the soul, it's only right that I make one here. I am not a good role model for the advice I'm foisting on you. I have not devoted the time I should have to deepen acquaintances into true friendships or to stay in closer touch with the old friends I do have. I've let the call of work get in the way. I've told myself that jobs of broad responsibility mean that one can't get too close to coworkers and colleagues.  I regret it and I'm the worst for it. You can do better. The same research that is documenting the loneliness epidemic reveals ways to immunize oneself against it. Geographic rootedness makes a difference. People who live in the same community for extended periods are far less likely to be lonely. A great CS Lewis wrote, friendship is the greatest of worldly goods, the chief happiness of life. If I had to give a piece of advice to a young person about where to live, I would say sacrifice almost everything to live near your friends. Having a religious affiliation also correlates strongly with feelings of connectedness. And nothing statistically reduces the chance of loneliness more than marriage, especially marriage with children. It's a sermon for another Sunday, but I do hope most of you will not miss the joy and the lifelong education of raising children. I promise it's life's greatest reward and the best graduate school you'll ever attend. I heard about one wise guy commencement speaker who instructed his audience, remember in life it's not who you know, it's whom. I thought of that as just a joke, but in our new situation it takes on a more serious tone. A lot of your success and happiness will depend on whom you know and know well. The author Gore-Vidal once said, we never know when we are happy, only when we were. I hope in your case that proves way too cynical, that there'll be countless moments when you are truly happy and know it. But I also hope that when you reflect back on times when you were happy, your days at this university will rank high among them. And when people ask you, what was it about Purdue that made it such a great place for you? You can just answer, well, I guess you had to be there. I know great achievements lie ahead for you. My wish for you is that so do great friendships. Hail Purdue and each of you. Exercising the authority of the trustees of Purdue University, legally vested by the people of the state of Indiana and upon the recommendation of the Purdue faculty, I now confer upon you who have fulfilled the highest requirements of this university. I now ask our orders to read aloud the names of all students receiving doctors of audiology diplomas. Graduates, I invite you to have your special designee place your hood and present your diploma to you now. Be sure to capture a photo of this to share with us. I now ask our orders to read aloud the names of all students receiving doctors of veterinary medicine diplomas. Graduates, I invite you to have your special designee place your hood and present your diploma to you now. Be sure to capture a photo of this to share with us. Kala M. Boulevard, Sarah E. Canada, Cassandra L. Chakowsky, Emily A. Christensen, Hannah M. Clinton, Mallory C. Klaus, Brittany A. Crenshaw, Kristy M. Crow, Jonathan M. Daniel, Joseph W. Denverow, Brooke Dodge, Katie J. Elkins, Carolyn M. Ellis, Destiny L. Evans, Haley M. Everett, Catherine L. Fischbach, Katrina Frerichs, Trey A. Gelert, Caitlyn L. Geneggi, Andrea J. Grasso, Carly M. Gunlock, Andrea R. Hall, Anadel R. Harris, Ashley M. Hopkins, Thiela L. Horn, Daniel L. Keating, Michelle E. Kelly, Ryan J. Kelly, Katherine L. Kelsey, Amanda M. Kemper, Megan Siekenauer, Allison L. Collowitz, Kerry M. Lasala, Stephanie S. Lewis, Alyssa R. Like, Jessica E. Linder, Sophia Lopez Vale, Danielle Lauer, Anna M. Marshall, John M. McAfee, Walter John McGowan Howard, Danielle N. Miller, Ezekiel Montanez, Stephanie L. Morgenstern, Julie R. Mosher, Lauren L. Muscat, Nicholas M. Newsom, Jenny N. Nguyen, Garanty O'Rear, Sherry S. Park, Diana C. Fitta, Zachary C. Reddy, Nathan C. Ritzler, Sarah Rorrabaugh, Kylan M. Roman, Anna DeSacco, April L. Schnatter, Casey L. Shulke, Nora J. Sloan, Katelyn H. Smith, Christina J. Smith, Levi H. Smith, Samantha L. Schwartzentruber, Megan Swayze, Kristen E. Thomas, Brent A. Unruh, Brooke A. Unruh, Chad M. VanCourt, Jessica L. White, Jesse M. Whitfield, William M. Willis. Exercising the authority of the trustees of Purdue University, legally vested by the people of the state of Indiana, and upon the recommendation of the Purdue faculty, I now confer upon each of you who have fulfilled the stated requirements, the appropriate baccalaureate degree and associate degree, with all the rights, privileges, duties, and responsibilities of that degree. As evidence of your achievement, you will now receive an appropriately inscribed diploma. I now ask the orders to read aloud the names of all students who are earning baccalaureate and associate degrees. Graduates, I invite you to have your special designee present your diploma to you now. Be sure to capture a photo of this to share with us.  Okay, it's official. You are now graduates of Purdue University. It's time for you to observe the tradition of moving your tassel from the right side of the mortarboard to the left, signifying your new status. Congratulations to all our new alums. It's appropriate at this time that we give special recognition to those graduates who have demonstrated outstanding scholastic achievement, leadership, and service to the university community. You can read the names of the recipients of those recognitions in your commencement program. Additional special awards have been noted on your division's ceremony page. On behalf of the trustees and the faculty, congratulations on your accomplishments. We're counting on you to continue making exceptional contributions in all your future endeavors. Some of our graduates receive red, white, and blue cords, signifying that they are veterans of the U.S. Armed Forces. They deserve our recognition and our sincere gratitude for their patriotism. We honor these graduates for their hard work and accomplishments, but they'd be the first to tell us they did not make the journey alone. So will the graduates please take this moment to offer thanks and appreciation to your parents, grandparents, spouses, brothers, sisters, and other family members and friends for their long and loving support that brought you to this moment. The audience is invited to sing the Purdue hymn. The words are located inside the front cover of the program and will be projected. Will you please stand? I'm pleased to introduce Jordan Whaley, who was selected as today's student responder in recognition of her leadership in many student and campus organizations. Jordan's receiving a Bachelor of Science in Nursing and plans to work as a hospital RN. Please welcome Jordan Whaley. Hello, it is an honor to be able to share this special experience with you all today. I would like to thank President Daniels, the Board of Trustees, and all of those who made this possible. While this commencement is not what we expected, it is uniquely our own, and for that I am grateful. It took me a long time to reconcile with the fact that I was not going to walk across the stage and receive my diploma as planned on May 16th. I was angry, and it made me more angry that I did not have someone to blame. Whose fault was it? The professors, President Daniels, the virus itself? These thoughts of anger, frustration, and sadness were taking up all the space in my head. This sense of loss, however, was not unfamiliar. I found myself reflecting back on what I thought was going to be the defining moment of my college career, which I'd like to share with you today. In the fall of sophomore year, my dad passed away after a nine-year battle with early onset Alzheimer's disease. My dad was a fellow Purdue nurse, even though he was a Purdue engineer first. It is my greatest joy that I get to share his passion for helping people, even if I do not get to talk to him about it. In September 2017, my life had been completely flipped upside down. This was not how it was supposed to be. I was angry and heartbroken as I grieved with the loss of my father. But I made it through, and I climbed out of that dark valley with a lot of faith and with the Purdue community surrounding me. I needed those around me to help me thrive again in college. I needed family, friends, advisors, professors, and other Purdue staff members. There were people in every corner of this university ready to support me no matter what. If there is one thing that I learned that fall semester, it is that Purdue is a place where you do not have to be alone. The Purdue network that I formed mourned with me then, and I am confident they are mourning the loss of our usual spring traditions with us all now. It seems that we lost our last eight weeks as seniors at Purdue. This was not how it was supposed to be. We lost those precious last few moments on campus, but we have not lost the title of Boilermaker. The world is an ever-changing place, and through this pandemic, I believe we all have learned that we are not guaranteed anything. Yet we do have control of how we react to this new normal. Even though we are not physically together right now, we always get to call ourselves Purdue graduates, and with that badge of honor, we get to claim the Purdue family as well. People to mourn with us, people to support us, and people to cheer us on. COVID-19 is not going to be the only thing that destroys the what should have beens. We are going to have many defining moments in our lives, both good and bad, that we will be forced to deal with. I thought losing my dad in college would be the significant event of my four years, but my story is proof that life does not stop. My hope for us all is that we cling to the skills that Purdue has given us in order to push through our future defining moments, as any Boilermaker would. We will push through, and we will flourish. There is a quote written on the Berlin Wall that I often think about. Many small people in many small places do many small things that can alter the face of the world. We will be many small people in many small places as we start our careers. But I know the Class of 2020 will alter the face of the world as Boilermakers. I am proud to be able to share the distinguished title of Purdue graduate with my dad and with all of you. Congratulations, Class of 2020. Thank you. Today you are standing on the edge of possibility. We pause to give thanks for the deep sense of accomplishment, pride, and hope of this very moment. We pause today to reflect on your time here and the hope for your future. We gather with abundant joy and overwhelming gratitude. We pray the experience here embodies her vision of education as an engaged and dynamic partnership between the university and the wider world. Her work here has never been merely for personal use, but always for public good, and so today we celebrate the collective commitment to creating a better and more just world. May we appreciate the joy of this day and the dedication and patient perseverance that has brought us to this moment carry us forth to create a world marked by peace, justice, and compassion beyond measure. Go forth to do the good work set before you. Be tireless. Seek the common good. Love and nurture the people and things you value. Attend to your work with integrity so that what you believe may be what you do. And when you feel as if you might become defeated, think back on this day and know that you already have accomplished much. May you be blessed and may your lives be a blessing to all of us. Amen. As we conclude today's commencement ceremony, thank you for joining this celebration of our newest Boilermaker graduates. On behalf of the trustees, faculty, and all the Purdue family, I bid you farewell. Godspeed. Hail Purdue.


I felt games with tweets from a guest speaker who's in Singapore forward a couple of weeks. Yeah. So let's start with Abel, and then we'll have a second one with Lucas talking about... What was it? Quick chat. Quick chat, yes. The only one quick announcement is I won't be around until the end of the meetup. I have to rush off halfway through, but a friend of mine will be sort of helping to clear up after. So if you have any questions, feel free to ask Mike, or I have to be sorry, feel free to ask Mike, his member as well. Otherwise I'll see you guys next time. Thanks. Go ahead. So my name is Abel. I'm going to be talking about some work that some people did when they tried to predict the outcomes of NFL games using data from Twitter. My English is not that great. I mean, it's kind of accented. So if there is anything you don't understand, please don't be afraid to ask for repetition. If you don't understand, probably most people don't either, so they're putting them in favor.  I hope it doesn't. This is a good time. Also, as I was reading the paper again on my way to Singapore, it kind of turned a bit into like a papers we hate presentation. They have that spread often actually. Hopefully it's still a good time for everyone. So first thing is, so NFL is an American football league. Here usually makes some kind of European smart comment, but I'm going to be nice. So this is a sport. Things you need to understand to kind of follow the rest. Basically there are two teams on every game. So one of the teams is the home team, which is the one that has the stadium. You know, they control the stadium or they play their health of the games. And the other one is the away team, who is visiting. So that's a good thing to know. And then the teams get points, whoever gets the most points wins. And it's a weekly schedule.  And then you have another bunch of games that are played on week two and so on. So that's pretty much everything you need to know to understand what the analysis of these people did. And something that may be good to have in mind as well is that there is a ball. And if the ball goes in one direction, that means one team is like more likely to get points. And if it goes in the opposite direction, it's the other way around. So that's kind of good knowledge to have as well. That's it. So I forgot to say this, but if you have any questions, feel free to interrupt. I don't have the many slides. So it's not like it's going to go over time or anything if you ask questions. So feel free to do that. So now I'm going to talk a bit about the kind of predictions that these people make. Basically, at the end of the game, you have a score. And there are several kind of things that you can try to predict about the score. So the first one is the winner. So just which of the teams has more points in that score. So that's an easy one. Another one that's kind of like more subtle is a prediction about a 50-50 spread. So basically, some people don't like to make predictions about winners because I can get winners right most of the time if I just say that the home team is going to win. Because home teams win most of the games. Some people would rather make predictions about things that are 50-50 split. I think the reasons have to do with variance, but I don't really understand them. But it doesn't really matter. The point is that happens. So what happens instead is that the people that accept the predictions, which are usually like casinos or bookmakers or websites that people do for these things, they set a spread. What that means is that they see, okay, we have a team that's the favorite. And then they ask these questions. Using some internal model, what's the chance that the favorite is going to win by one point or more? What's the chance that the favorite is going to win by two points or more? What's the chance that the favorite is going to win by three points or more and so on and so on? And you keep asking the questions. Those chances get closer to 50%. So you pick the question that's closer to a 50-50 split. And that's the spread question. So basically making a prediction about the spread is giving an answer to a question that looks like that. Is the home team going to win by three points at least? That three is the spread. That's why people call the spread is the number that's associated with the question. So is this more or less clear or should I? So you're saying that that's the number. So three, if you see the spread of three, then three is the number where you don't know... It's a 50-50 chance. For one of the teams, yes. So saying that the favorite team will win by that number of points or more is a 50-50 proposition. And that's people call that the spread. And then the third one is over under, which is a prediction about the total number of points that are going to be scoring the game. So similarly speaking, there is a model that the bookmaker or casino or wherever has. And then they determine which one is the 50-50 point such that asking is there going to be more than 200 points is a 50-50 proposition. So those are the three kinds of predictions that we are considering. Now I'm going to talk a bit about the information that the people in this paper used to predict the games. So it's not just tweets. If you just use tweets, it's not that great. In many cases, you need to use other things. I don't know if they started doing that at the beginning of the paper or they did some experiments and realized that tweets are kind of shit sometimes. And they go, I don't know how it happened. But the point is they actually use quite a bit of other things. So the things that they use is the first one is just the frequency of words in tweets. So for each word, if that word appears on a lot of the tweets that people make about a game, the percentage of tweets in which that word occurs is something that they use. So basically, if everyone is tweeting about a team that's playing a game, the word injury, that probably means they're going to do worse. And if it's an offensive player, that might mean they're going to score less points. So it might be useful information for making a prediction on the over-under. So that's an example of how could that be useful. Something else that they use as kind of a smart is change in tweet volumes. So how much are people tweeting about the game? I guess you could say to give some intuition that maybe if the fans are not tweeting much about their team, that might mean they're not very motivated and the team might do worse than expected. So that might be a possible way of causation. Also, something that they use is the actual dose spread and over-under 50-50 margins we've been talking about before. An obvious way in which this is useful is if the spread is asking you, is the home team going to win by five points or more, then you can use that to make a prediction about the winner and say that the winner is going to be the home team because that's implicit by getting asked the question about the spread. And also, of course, they use some statistics of things performance like maybe they have been, maybe both of the teams have been scoring a lot of points lately. So maybe that means the over-under might be more likely to be over than under. So that kind of information. So a little bit more about the details of how they get that data. Often I think with the kind of project, the hardest part for actually doing it is actually getting the data. I wouldn't be surprised if they spent most of the time just getting the data and then they ran the analysis in like one hour. That wouldn't do performance. So the tweets they get from the Twitter garden hose API, I don't know if that's open or not anymore, but that gives you like 10% of the tweets that people make to associate the tweets with a particular team that's playing on a game to see which game it is, you just look at which week it is. So basically it's the next game that's going to happen. And then to see if it's one team or the other, they use hashtags. So if it has the hashtag for more than one team, they just ignore it. If it has hashtag that they manually see which hashtags correspond to which teams, then they associate it with the team that they hashtag. So this should be associated with. And pretty much everything else is from the website nflata.com. They also collect some information about the spread lines that the casinos are setting. That's where they take it from as well, from the website. So as I study something that they do that's kind of cool is they look at which words appear on tweets after games are played, depending on what the outcome of the game is. The coolest thing that they find is that when the VC team loses, people complain about their queries. They have the word refs. But when the home team is the one that loses, they don't complain about it's not so significant that they care about the refs. And that's actually kind of correlated with something I mentioned before, which is that home teams will win most of the games. People have studied why is this the case. And the consensus answer from several studies are under the impression that in most sports, home teams are the ones that win most of the games because their referees favor them. Subsconcially, you could say that it's just because the crowd is there and yelling you to some sort of something, you're more likely to do that. You don't want to go against the crowd. So it does make sense that when the VC team loses, they complain about the refs. It's more or less proven that the refs are going against them, that when the home team loses. So that's kind of interesting. It's a little section that they have in the paper. So now before you put that data that they collect in that way in the prediction model, there is some data massage that you have to do. So basically it's too much to have a feature in your model to have a variable for every word that appears in all tweets that anyone has made about that team. So they just take words that appear in a fixed percentage of the tweets. It's a little thing that they do. And something else that they need to do, kind of messy as well. But basically they do some predictions where they use as variables both those frequencies of words in the tweets and some information about the performances of the teams. I haven't looked at it, but I would expect that there are like tens of thousands or like thousands of variables corresponding to the frequencies of particular words and the performance of the teams that they are using like 20 words. So if you just mix those tens of thousands of variables with the frequency of the words with the ones about the team statistical performance, which are very few, basically your model is pretty much just going to ignore the ones about the performance of the teams. They're just not going to get drawn by the other 20,000 variables. So then what you need to do is to combine those 20,000 variables into a few that are in the same order of magnitude as the ones about the teams' skills. So basically what they do is that they consider word frequency for a particular game as a vector. So if the first word in some fixed order appears in 20% of the tweets and the second one appears in 10% of the tweets, there is going to be like 20, 10 something. And then they use a linear map to reduce that vector to a smaller dimensional vector. And then each of the components of that vector are the new variables. Is that more or less clear? Or should I repeat? And that's another trick that they need to use. And then something else that they do is that to look at those changes in volume of tweets that we've been talking about, you can look at those changes of absolute quantities, like there were 1,000 tweets less about this team this week, or you can look at it as a relative thing. There was a 5% decrease. So I don't think it's clear which one to use. In different situations. So the next thing to mention is which algorithm do they use to make those predictions. So it's logistic regression classifier. So basically that's what the logistic function is like. For every variable, that's more or less simulating a threshold function. And basically what your model training algorithm is doing is associating a weight with each variable and a threshold. And then if the value of the variable for a given game is about a threshold, you add that weight to your estimate of the outcomes. Of course it's not exactly that because it's a little bit more smooth, but that's basically what it's about. So basically in this case, the x-axis is any of the variables that we talk about. So it could be the frequency of a particular war in the tweets that are made about the game. So the frequency of the war attack in tweets about the game. And then the y-axis. So basically what you're doing here is you are fixing one of the outcomes of the prediction. So you're saying arbitrarily that you're trying to see what's the probability that the over and under is over. You could do it with under instead. And basically for each variable in your model, you have a curve like that. The y-value is how much probability you add to your estimate that the outcome is over based on that variable. So of course this could be symmetrical. This could, instead of going from 0 to 1, this could go from 1 to 0. And you have a weight for each variable. So it doesn't go from 0 to 1 because from 0 to the weight. But basically you're just going to have a curve like that for every variable. And then you're going to go, when you're making a prediction for the game for each variable, you will go to this graph. You're going to see what's the y-value corresponding to the value of that variable. Or you're going to add that to what you think are the chances that the outcome of the game will be over in the over and under case. Sorry? Yeah, that's the probability. You're basically trying to find out which variables are more important than the other ones. Yes, exactly because the variables that are more important are going to have a larger weight. So the variables that are more important when you do that process, the value that you're going to get is going to go from 0 to maybe like 0.1. So the curve will be shifted? Well, the curve is also going to be shifted but I'm talking about something different. I mean I'm talking about a vertical scale rather than a step. The size of the step. Yes. The step will go from 0 to 0.001 if it's a variable you don't care about because that variable doesn't impact your prediction. If it's a variable you care about, the curve will go from 0 to like 0.1 because it accounts for 10% of the interpreter of the answer. So do they run the model and then for the first time they run it, they don't adapt one variable and the second time they run it they check the other variable out. How does the learning process work? I live night one by one, the variables. I'm not sure how the learning process works. I'm not sure what's the best way to train this kind of machine learning algorithm. Something like that maybe what's going on. They should be throwing everything inside the model and then they might be training with the sigma function with the 1 divided by 1 plus e raised to minus beta 0 plus beta 0 x1 plus beta 2 x2 like that.  For the model itself it's basically through every scenario. So it will be covering for all the scenarios and then you take the budget of the function and then you try to predict the output which is from the 0 to 1. What they do also, what's the best thing to do to train the model depends on the amount of data that you have because you cannot afford to examine too many combinations. You have a lot of data because it will take too long time so you need to be smart. But I'm not sure exactly what. I don't think they implemented that themselves. I'm pretty sure they just used an off the shelf library for that. It's not on your net or something? No, it's not on your net. Probably there is some math that's related in some parts of it but it's not on your net. They just use registry reflection. They don't consider alternate. They do a lot of work on which variables do they throw into the model but they don't do much work into which model do they use. They just take this one. Have you tried other classic models? I don't think they don't do that in the paper. And do the papers are wide? These ones might be different questions. I assume it's kind of a standard. I mean I remember doing similar issues that before and I just used logistic classifier because that was the easiest. It cannot make sense. But do you think they throw a lot of care into which variables they put into the classifier? Yeah, I threw a table. Do the variables sort of effectively then manually select some variables to put into the classifier to see which gives the best output rather than taking a selection of variables and running it through between intelligence or statistical things to find out which work best? It's kind of a bit more brute force, is that what you're saying? They do it kind of by hand. Well, they do it kind of by hand but not quite because there's actually another paper that they cite where by brute force they are using an algorithm. So there are three kind of, actually they just classify variables into two kinds of variables. There is the Twitter variables which are very few and they are very few. You have one for each word but you have the Twitter variables which is the one that this paper is focusing on. Those, basically there are three cases. One case is when they consider the Twitter frequency of the words and they don't consider anything else. Another one is when they consider the Twitter frequency of the words and they do that linear reduction and then they throw some features of the performance on the teams. And then I think with the rate of tweets they also consider it by itself and combining with some features of the performance of the teams. Those features of the performance of the teams, they try several options but the combinations of options that they try are taken from a paper which itself has an algorithm to look at which combinations of variables are best. So it's not completely arbitrary their choices of variables. They took it from some paper that has an algorithm where they try to be smart about looking for sets of variables corresponding to the teams performances that can be used to predict. Does that answer the question? I think so. So the way the predictions are made is in an online fashion. So basically they are starting week four of the 2012 season because the first weeks of the season are thought to be kind of random. This kind of weird stuff going on in the first week. So they start in week number four and then they train the model for each week using data from the two previous seasons. And this current season when they read the paper which is used as a 12 up to three weeks before. So the value of the input variables as well as the output of the games for all of those games are what they use to train their prediction model. And also the prediction model has some parameters that are given to the training algorithm. So they just brute force was the best value for those parameters such that the algorithm makes good predictions on the previous two weeks. That seems kind of a hock. I mean it seems reasonable your kind of train to adapt to local trends. Maybe you should choose it to maximize performance in the whole set. I don't know. But that's the way they train their prediction algorithm. Any question? So then I'm going to discuss the results that they get. So basically if we look at the predictions that they make about who's going to be the winner. Many of them don't look very impressive because as I said if you predict the whole thing is going to win you're going to get it right 57% of the time. So if Twitter Unigrams are giving you 52% or if Twitter Unigrams combine with the F whatever represent the information about the performance of the teams except F1 and F2 that represent the spread and over on their lines. So if you combine that information with the performance of the teams F something with the Twitter world frequency as one variable and you get 47.6%. That's not very nice. So I mean I can understand where they use the Twitter rate variable which does affect that. So something else that also as you can see you can use a point in the spread line to see who is going to win. We're going to win the favorite. So the point in the spread line is that is a 50-50 proposition that a team will win by five or more. Well that thing is going to win most likely and you get that right 60% of the time. So that's kind of cool to see. That's F1 at the very top. And then there is something that's kind of annoying me which is they don't put any noise levels on this. So let's forget for the rest of talking about this slide, about the winner column. WTS by the way is with the spread. So it is the 50-50 spread prediction we've been talking about. So basically here what we have is 32 predictions about events that are more or less 50-50. You have 16 algorithms and you're making predictions for two things each. So you have 32 series of doing predictions. So I think something that you should consider is let's assume that our algorithms didn't have any prediction power at all. Our algorithm just like through a coin. And then we have 32 runs of that algorithm that has no prediction power. Then what's the chance that one of the runs by luck actually gets a bunch of gains, right? So I made those numbers and hopefully they're right. So basically I think you're expecting to achieve 53.6% in 5.5 of those runs. Even if your algorithm has no prediction power. And we can see here how many get 53.6%. It's like 1, 2, 3, 4 and 5. So maybe I missed one but like it's kind of disappointing. I like the idea of using Twitter on one note but it would be cool if they kind of acknowledged that. And also how many are expected to achieve 57.2% is like 0.8. And there are two that do it. So that's a little bit more optimistic. Something that stands out is that the best number tend to be the ones that use the Twitter rate, the rate of tweets. Combine with some features about the team's performance. So that's just six predictions. So the success that we see that 58.2 and the 57.2 just for making these predictions. It might be worth looking more into. So that one is a little bit better. But overall the results, I mean, again, at the 53.6 level, they're not doing better than random.  So something else just kind of like winding down the presentation a few things. So first, if anyone is wondering, okay, I'm going to implement the Twitter rate thing. And maybe if I get lucky, I'll make money. So some things that might make that kind of hard. So one is the market just into information. This paper is not from yesterday. It has one or two years, maybe three. So the people that set those spread and over underlines, they read this paper too. So they might have taken that into account to the way they set the lines. Or maybe not. Maybe they have all the reasons that it's best for them to ignore this. But there's a chance that they took it into account. Second one is that these people are getting those spread and over underlines from NFLLate.com. Sure it's there, but that doesn't mean that that does a snapshot from a website. That's from a particular place at a particular time. Those might be the spread lines that a particular bookmaker that you might not be able to read that is giving just before the beginning of the day. If you're making a prediction, you're usually not making just before the beginning of the day. Or whatever point the NFL data took the spread lines from. So it might be a different mathematics for adjusting to the spread just before the game and for adjusting to the spreads one week before the game. And then of course there is lack. There's a lot of lack involved in this kind of stuff. We've seen in the previous case with the random coins. So one more thing. If this talk was interesting, I saw that there is a course in EDX, a related thing. In my suck, it might not. I intend to start it. I thought people might like it. This is the name of the paper, the address. I thought it would be cool to recommend this Twitter account. I think it's not about NFL, but it's about, well, it's about fan stuff, but it's also about Mark of Chains. They have, I don't know if anyone has seen it here, but basically they combine emails from recruiters for like software jobs. With, there is this other website, Collero with, I don't know if anyone has heard of it. It's a website where people talk of their experiences with like chemical compounds. And then they combine it. So they basically look for like ways that you can make sentences that make sense. Taking part of one, taking part of the sentence from a repeat and taking the other one from an email from a recruiter.  I like it. I can put it on the screen as well. I can put it on the screen, so you don't have to look at the phone. So, I don't know. I think I like this one. They have a lot of them. I like this one too. It's kind of simple. The vast parking lot I was planning. Guy, I hope you're in the hospital for the next couple of days. I will reach out. That's kind of nice. Oh, I like this one. Codating a state of categorical anxiety and directionless fear until six seconds. Don't hesitate to reach out to see if you would like to set up a brief questionnaire and possibly linger in psychological difficulties. Oh, I like this one. Stand for alumni with a large amount of psychological preparation, extensive practice in meditation and identity control. And I'm a book. This is the top. This is probably the best of all time. Morning, holding ourselves to quell the inner pain. We are experiencing incredible pain. It's very nice. I'm actually very curious about why they use unicramps. Because I feel like... That's good. Because I feel like a lot of information is lost in human speech with words that negate meanings, for example. I mean, that's the very basic one. You know, sarcasm or... You probably get a lot more information there. And I think if you mind that, there's much more accuracy between that. And Sandinia was thinking also as pronouns. It's not the same as you say, we lost. Then you just say, you lost. It makes a big difference. They didn't really do any, like, sentiment analysis. Exactly. No, no. That's the next level already. How positive sentiment is. I mean, they are expecting that the sentiment will be inferred by the training algorithm. They will expect that the... I mean, it's reasonable, I guess, to assume that the training algorithm will detect that the war injury in the tweets of people that follow our team is correlated with the team, like, not winning the next game. But it's not strictly speaking sentiment analysis. But yeah, it's just an emergent thing that might happen. But they are not being very... It's not explicit at all. That might be interesting. Making money, I always feel like this is very interesting to do. But at the end of the day, if somebody is feeling about this, somebody else is thinking about it. That person probably also knows that this team is going to lose or can also expect that. Yeah, I mean, the thing with the sports predictions is that the mark, who do I say this? There are both rational... I know I'm not an expert in economics, but a way to phrase this that hopefully doesn't sound too stupid is there are both rational and irrational agents in this market.  And those people also have... They also can move the market. So that makes things a bit complicated. The expectation would be that Twitter would probably be quite closely aligned with the prediction of the market in terms of the betting market, because I believe most of the bookmakers adjust based on how many people are making bets on either side, which is basically... It's kind of like crowdsourcing the answer. And this is almost doing the same thing using Twitter. So I'd imagine that generally the correlation between crowdsourcing of bets and the crowdsourcing of Twitter. It has a higher degree of correlation, but a higher degree of correlation in likelihood of being wrong just as much as likelihood of being right. I think the bookmakers reacting to supply and demand in the buying and selling is possibly a lagging indicator. But I think they're saying this could be a leading indicator of the performance before the bookmakers maybe want to stay behind the leading indicator of the social media. I cannot tell you anything about this book because I'm just there and rich. She's like, why is it possible? They're just there on the basis of how much they're making bets. So it'd be interesting to see it. Well, that's why they're adjusting to minimize their risk. So it'd be interesting to see that actually plotted over time to see if you have a graph of where the market is for a particular event as it nears D-day versus this algorithm and how that plots it to see whether or not there is a time series lag or lead. How much was the amount of the tweets, the numbers? Was the number of tweets? Because it matters also if you have like 10,000 or 10 million tweets. Maybe on the paper. Maybe on the paper. Let's see if they have it. They use large quantities because that's good. Okay, no, it's here. So pre-game. I think weekly is the one we're looking at. Anyway, it's on the hundreds of thousands. I mean, you're dividing that between a few hundred games but I think it's still, I think it's still more or less significant. Any more questions? So about Mark's options. I think maybe you should check out PsyGen. There was the classical Mark's option of whether the MRT used automatically generated scientific papers and transmitted various kinds of things. And we found out, I think, that the suckers were the philosophy of PsyGen. Yeah. I've actually read the post-modern generator which is based on PsyGen. And it's actually quite believable. Yeah, and it was in charts as well. Charts and tables. Is that the Sokala fair one? Yeah. Is that the post-modern one? Yeah. So there were a few people a few years up, more recently than that, that actually got into some confer... I triple e-conferences. I mean, they were like very sketchy I triple e-conferences, but they got into them. It's just nobody read the papers. Yeah. What are the predictions? I'm assuming they were done before the game, and it wasn't like a bad test. They only use previous data. So I don't know if they did it every week or at the end of the season, but the predictions about a particular game ignore any data that happens in the future. I don't know if that's done because the prediction is actually made temporally speaking before the data exceeds or because they don't just take it into account, but it's done in an online fashion. It's not... I don't know. Was it all done in retrospect? Yeah. Even if it was not in a batch test, the prediction... it shouldn't matter, really. No, because you're ignoring the future data. If we look at what they do, either they did it every week or they simulated it, because basically for every... so the games happen week week one, a bunch of games, week two, a bunch of games, and so on. So what they do is, okay, let's try to predict the games from week five, and then let's train our algorithms using data up to... the previous week, up to what happened on week four. And then let's make our predictions. And then, okay, now it's week six. Let's train our model again using data up to week five, and then let's make our predictions for week six. If you do that at the end of the season or you do it in each week, it shouldn't matter because... If you're not using anything faster, the game... There are no more questions left. Thank you.


And now, I would like to welcome Taylor Dolizal from our top end user, to provide our top end user award ceremony. Please welcome Taylor back on stage. Well, Averon, we made it to Friday. How are you doing? Like, huh, huh, like, huh, OK, we could talk about that one. It's my favorite session of KubeCon, getting to hand out awards. Our top end user award is really special. It goes to a community member that has been working a lot within the space, coming to meetings, showing up, contributing, and helping out others. This time, so this award is submitted by community members, and it's selected by community members as well.  It's like, I'd like to ask them to stand. Thank you very much. We're very proud of your contribution. We're incredibly happy to have you within our community, and congrats on the award. Yeah, I'm really excited, obviously. Well, that's it for me. I guess I'll see all of you next KubeCon in Chicago, and we can dish out some of the latest cloud-native topics. Fuck, some pep in my step as well. I'd like to introduce to the stage, well, I do have one more gift for you, the gift of GAB. I would like to introduce to the stage Gabrielle Colombo, GM of the Linux Foundation Europe. Thank you all so much. I'll see you in Chicago. Thank you, Taylor. Thank you, everyone. When I learned what gift of the GAB meant, it was pretty apropos. Anyway, thank you, CNCF, for giving me the chance to share a call to action with the open-source European community. For those of you who don't know, Linux Foundation Europe was launched last September in Brussels to help create and grow projects in Europe. Now, little disclaimer, I'm sure legal stuff is exactly what you wanted to hear on a Friday afternoon in Amsterdam after a week of conference. But the Cyber Residency Act is coming up. For those of you who don't know, it imposes requirements for software producers and hardware producers to bolster cybersecurity in Europe. And we certainly commend the goals, but there is a broad consensus amongst open-source communities. We just signed an open letter as a Linux Foundation that it might impact the very fabric of the open-source community, containers, foundations, potentially package managers. Now, the bottom line is that professionally I'm worried about the impacts on open-source. And personally, as an Italian citizen, just more broadly about the thriving European technology ecosystem, which so much relies on open-source. I'm not going to bore you any more with legal stuff, but if you get a chance, join us for a panel and Birds of the Feather at 11. We have, amongst others, Greg Crow Hartman, who's the stable kernel branch maintainer. And if you want to get in touch and take action, take a look at the QR code, get in touch with your left, whether you're an individual or in your corporate capacity. We'd love to hear from you. And with that, thank you so much. Thank you all.