Okay, so let's get started. I hope all of you had a chance to have a break. What are you talk with each other or decided to have a quiet break? It's my honor to be moderating the upcoming panel here. For those of you who have just joined, my name is Leila Zia. I'm the head of research here at Wikimedia Foundation and one of the co-organizers of Wiki Workshop. And in the next 45 minutes, I'm going to be talking with the four speakers of the panel that you see on the screen and I'm going to introduce them to you momentarily about inclusive collaboration towards high quality digital common knowledge. So let me introduce the speakers to you. Jerome is an assistant research professor at the French National Center for Scientific Research CNRS. He's a research affiliate at the Center for Law and Economics at ETH Zurich and a faculty associate at the Berkman Klein Center for Internet and Society at Harvard University. We have invited Jerome here because he has, his background is in behavioral economics and his research primarily operates at the boundaries between psychology, economics and computational social science. His current research interests are on studying how people deal with their decision making biases as well as the determinants and consequences of communication style and leadership in virtual teamwork. We're glad to have you here, Jerome. Our next panelist is Benjamin Mako Hin. He's an assistant professor in the University of Washington Department of Communications and a junk assistant professor in the Department of Humans at the Center for Design and Engineering and Computer Science and Engineering and affiliate faculty in the Center for Statistics and the Social Sciences, the E Science Institute and many more places. Mako is also a faculty associate at Berkman Klein. He studies, he and his lab study collective action in online communities, and they seek to understand why some attempts to build collaborative production networks like Wikipedia or Linux work well, while others do not attract volunteers. Our third panelist is Cristina Lerman. Cristina is a project leader at the Information Science Institute at the University of Southern California. She holds a joint appointment as a research associate professor in the USC Bitterbee School of Engineering's Computer Science Department. Her focus is on applying network and machine learning based methods to problems in social computing, and we're glad to have her here, particularly for some of her recent work on cognitive bias and its impact on collaborative networks. Our last panelist is Misha Teplisky. He's an assistant professor at the School of Information University of Michigan. He's an alumni of Wiki Workshop. We are really glad to have him back. His research is at the intersection of science, science of science, sociology of organizations, computational social science. He studies how social and organizational factors affect scientific discovery. Previously, Misha helped postdoc at the Laboratory of Innovation Science at Harvard, and he received his PhD in sociology from University of Chicago, where he was a member of the Knowledge Lab. Thank you so much to our panel for showing up. And with that, I'm going to switch to the first series of questions. And as a reminder for the audience, the first 25 to 30 minutes will be us, the panel discussing different questions, and then we're going to come back to you for the last 10 or 15 minutes. For questions, please continue raising your questions in the chat. Isaac will keep track of them, and you can raise them when it's your turn yourself or as I can read out your question to the panel. So, Christina, I would like to start by you. So, as we all know, Wikipedia has been a platform for process and collaborative network collaboration over the past almost two decades. And today, while it serves as a project with high quality content, we also see a lot of challenges around the project. And that's kind of the topic of this panel is primarily focusing on how we can create an inclusive collaborative environment in Wikipedia that can help the project continue to have high quality content and provide it as part of the digital comments. I want to start by you partly because part of your research focuses on understanding the effects of cognitive bias on the solutions identified through collective processes. You have particularly studied stack exchange and I wanted to ask you to share with us some of the things you have learned through your research. Sure. Thank you. I'm going to try to summarize my talk in about four minutes. So, yes, I do study the interactions between individual cognitive biases that people use to make decisions and and the platforms algorithmic biases and how it impacts the wisdom of crowds applications. So, if you're not familiar with what kinds of biases are, these are actually based on the simple mental shortcuts or heuristic kinds of heuristics that people use to make quick and less accurate decisions. So these kinds of biases and introduce a kind of heuristic introduced well, well, kind of statistical biases into people's actions. So what types of cognitive biases and the psychologist actually enumerated more than 100 different biases of this shortcuts that people used to make decisions. But in the context of crowdsourcing implications, the ones that has your focus my attention on mostly position bias. For example, if you look at studies where people pay that we're on the web page, the people paying most attention to for Western cultures, it happens to be like upper left and mostly that upper of the pages. So whatever short information is shows up at the top of a page, more people will be more likely to see it. And then there was also social influence bias, you know, web, different web platforms, surf, show what other people like, you know, for example, number of likes or other kind of hot or popular items they highlight them to people. And then kinds of biases often interact with algorithmic bias and what the algorithmic bias that what I mean is that yes website either they show how popular items are or maybe they will actually rank items by, you know, they hope to do it by quality but most of the time they rank items by popularity. For example, the number one top ranked item is the one that is more popular, for example, the sort of items with the popularity. And we have actually explored this interaction between cognitive and algorithmic biases specifically in context of stack exchange. So because which happens, you know, stack exchange wants to get the community to help discover best answers to questions. And specifically both through a statistical analysis through online experiments on Amazon Turks, we found that people, instead of looking through and evaluating all answers to a question, people tend to focus on answers that are near the top, you know, they have more words or maybe they've been selected by the basket as the best question. And moreover, the more answers that are that are available, the more people rely on this shortcuts on this simple heuristics by choosing the path answer choosing the answer that has already been accepted. You know, in order to upward, you know, select the best answers. So then when when stack exchange actually aggregates all this decisions all this upwards from people in actually sub optimal answers might be the one that float up to the top rather than the best quality answers. So that's why I should agree. And also, in more recent research we're showing how these these interaction with algorithmic the way algorithms display items can actually produce unstable rankings in which items that are next time you run similar experiment for some different items will different answers will rise up to the top. So we are investigating also methods not only to quantify how much these biases interact how much the fact the quality of solutions the crowd discover, but also methods to try to reduce the biases and really enable the community to discover best quality answers. Thank you very much for that Christina. So I want to turn to you for our second question and I want to ask you to talk about your recent research that research that you and your, your colleagues have done. And in understanding the effect of bias on Wikipedia and how Wikipedia has managed bias in a productive way towards quality content production. Can you speak to what you have learned through this research. So everybody in Mexico. A quick rundown of the work. So this was a recent paper called wisdom of always crowds. And what we did there, myself and the team of sociologists information scientists be So after the sort of classic question of diversity diversity and performance, and particularly cognitive diversity, which is often really hard to measure and real settings of course, easier online and Wikipedia makes it especially a good case. So we're trying to understand the role of this sort of new wish or increasingly important dimension of diversity, people's differences on ideology. And so we did it, you know, so you might think that cognitive diversity is generally good for kind of like surfacing new ideas and that people combine them. And of course, we kind of like look around the world like ideology has this other component to where it's increasingly tied to our general identities, increasingly like effective polarization. Is that some people call it so, for example, if you ask people, will they marry a Republican or Democrat like increasingly people say no depending on who they are things like that. And I see how that's kind of new wish dimension of diversity plays out in Wikipedia. And so we looked at editors of 200,000 pages and try to understand their kind of ideological position based on what they what kinds of political pages they edit conservative ones or liberal ones. And so, with that measure in mind, what we found kind of surprisingly to us was that the more ideological, ideologically diverse the team, the better the page. And that was a pretty robust relationship that we basically couldn't make go away no matter what we tried. And it kind of works and I got to work in a, I guess predicted way where the relationship is especially strong where the domain as politics less so when it's kind of like less related to politics you know, social issues relationship is weaker but science pages even weaker but it still appears a little bit. And so the paper tries to like seeing this positive relationship which we don't see anywhere like see very few places in the world where the ideological differences are actually good. Or seem to be, you know, creating better products. So what drives this kind of pattern. So, you know, at a micro level, we see that people in ideologically diverse teams. We see fewer topics in the kind of semantic sense, but in more ways. So these more in a unique words to discuss a smaller sort of topics. So they're sort of like more concentration effect, I might say, and brought and sort of the rest of the paper and in general I think this kind of raised more questions that an answer. You see this possible relationship. Nice to see but what what sort of, why is it appearing here and not elsewhere. And so a few sort of survey you look around online spaces and I think a few things jump out which are sort of not things we answer in the paper but I think questions that the paper raises this. And one is sort of, there's a big self selection of people into Wikipedia. So the kinds of people selecting into that might have ideological differences but might be more open to teamwork and disagreements and others. So there's a question of like, self selection and brand of the media has and how well people go to brand and they choose to join or not. And then sort of another kind of a secret is that, you know, with the house, like one age one topic doesn't have pre pages from one topic. So that's what like forces everybody into the same sandbox, which is pretty unique and we don't see in other places like, you know, And I think class Lee and I think maybe more most like open to study and experimentation as the role of oversight, you know, as much more bureaucratic and much more kind of levels of oversight that again like Twitter and like that. So one question I think that was pressing in my mind is like, well, like how much oversight is good. We can eat it. So I think I'd say there's a good amount and it seems to have productive effects for this kind of sensitive kind of collaboration. Is that necessary or is that optimal I think is an interesting question. So that's sort of the rundown of our paper. Yeah, thank you for that. And I think that gives us a nice segue to Jerome's work. If we can talk with you if you can talk a little bit about more about your research in this space specifically as we talk about the oversight governance, moderation on Wikipedia. So you're part of your research focuses on the effective structures on minorities. Can you talk with us a little bit more about what you have learned in your research. Yeah, sure. Well, first, thanks for having me. So I think I'm going to start just to build intuition for the audience as to where I'm going with a couple of quotes, and then I'll tie back to what Michelle was saying and develop the argument that I want to make in those in those two minutes. So the first quote that I have for you guys is from Virginia Walt. It's a quote from the 30s. An essay that she wrote, which is called a room of one zone. So I quote, there was an enormous body of masculine opinion to the effect that nothing could be expected of women intellectually, even if her father did not read out loud those opinions. Any girl could read them for herself and the reading, even in the 19th century, must have lowered her vitality and told profoundly upon her work. There will always have been that assertion, you cannot do this, you're incapable of doing that to protest against or to overcome. That's, you know, from a quote from a woman, it's pretty old. Much more recent.  I think it was in astrophysics or something like that. The first one to get such a such such a degree in the US in the 90s, and he says, in this speech. In the intersection of society, my athletic talents are genetic. I am a likely murder or rapist. My academic failures are expected, and my academic successes are attributed to others to spend most of my life fighting these attitudes, leave these and emotional tax, that is a form of intellectual emasculation. Now, what does all of this has to do with Wikipedia. There's a significant gender gap and, you know, an ethnic minority gap on Wikipedia. And I want to point out right away that, you know, we must question whether this matters per se. Right. And I think it matters, because it creates bias, it lowers the quality of the content, just because people who could take different sense on a topic are simply not represented. It produces stereotypes when people look up for information online. And so basically my message in these two minutes is that psychology, maybe the most fundamental bottlenecks and the integration of ethnic minorities and women, as opposed to the bureaucratization of Wikipedia, which is real, but which is only an aggravating factor, in my opinion, and we'll probably return to that. So, you know, if we think about many of the users who are with us today, there are probably early, many of them are probably early enthusiasts who found it amazing that you could argue with people from all around the world for the better good. Right. And as Michelle was saying, Wikipedia has been amazing at creating rules that focus the debate on what on the debate on what people say, as opposed to who says it. That's their quality of point of view. And that have been so successful. Actually, 15 years ago, it's hard to go back, but nobody would have predicted that the very best articles on Wikipedia would turn out to be those that are highly controversial and attract a lot of different viewpoints. Right. Part of the featured articles on Wikipedia are the ones on Barack Obama on Jesus. That would have been totally unpredictable 15 years ago. Now, what I want to point out here is that this whole system of managing different viewpoints and, you know, political biases and opinions rests on contributors being resilient when they argue their points. That is, they're ready, or even they enjoy arguing their points for hours with each other. Now, if you got the intuition of those quotes that I had at the very beginning, my point is that this is not the case for many different strata of society, and in particular minorities and in particular women. And so that could be a major barrier to entry. And those are not only just quotes, because in the 20 past years of research in the behavioral sciences, we actually collected a very vast amount of experimental and field evidence for the relevance of those quotes in people's real lives. I don't care all the references, I'm going to add a bunch of research, but we know that women and minorities are much more risk averse than the rest of the population. They're much less self confident.  They don't like to compete with others directly. So in field studies in psychology and economics, they've shown that those differences. They actually explain a substantial corruption of students orientation choices as early at high school controlling for academic achievements. And even within a given sector, more competitive individuals are estimated to earn 10% more than others for the same qualifications. Particularly women and minorities. So I think that we should keep all of that in mind when we think about why do we want and how we want to address systematic biases that arise from lack of participation from certain strata of society on Wikipedia.  So, you know, just citing one and then concluding, I don't know how much I've been talking for now. I could talk more, but it's pretty obvious to everybody that when you go to college for the first time, it's pretty common for people to experience social setbacks, feelings of isolation. This is a challenging new environment far away from your family. But black students, they're much more likely to interpret this as evidence that they do not belong at university that this is something that's related to their identity to their race. And so, in one science paper, among several, what people did is that they just ran a quick social belonging intervention without those students knowing having a control and treatment. And they just manipulated them so that so as to attribute those setbacks to general factors that are common to everyone and absolutely not related to race. And the simple intervention creates very large difference in terms of their academic achievements three years down the line. They measure those academic achievements, but not only academic achievements, self reported health, depression, the accessibility of negative racial stereotypes, self doubts, and also subjective happiness. So, so my point is that, how do we make sure that women and minorities feel like they belong in a space where people enjoy arguing, sometimes even for its own sake, right. And this is much more complex a problem to solve than simplifying the contribution process at the technical level. That is create a visual editor or something it will require a lot of creativity, which should happen in the intersection between the behavioral sciences and platform design. And, you know, a few leads there we know that teamwork is efficient at having increasing women's resilience in the face of competitiveness. So we need you be to feel embedded in the social network right from their very first experience. Maybe we could think about a menu of health button so that newbies would feel like they're welcome and supported also right from the start and that this is not about a competition. And part of the challenge here is that it does not seem reasonable to expect this from active users who already have a lot on their plate. So the question to me is, how can we engineer such processes within groups of newbies that share some interest, how can we connect them while shielding them from self assertive or bureaucratic behavior that achieved so much in our domain of Wikipedia for our kind of population. I think this is what we need to tackle. Thank you so much. And Mako I think this brings us naturally to you and to your research and your labs research you focus on understanding the trade offs between engagement and quality. Can you share with us what you have learned through your research. Absolutely. So, I mean, so my research is really focused on life cycles around knowledge commons and online communities. And most of our work sort of builds on these builds on these sort of attempts to create data sets of lots of attempts to build communities like Wikis. So, in one case that involved looking at a whole bunch of the kind of online collaborative encyclopedia projects are created before Wikipedia with a way of trying to, you know, uncover those dynamics. I think much of it has looked at other wikis so big data sets of attempts of wikis from wiki or pre open software projects. Now, one thing that I suspect a number of people on the sort of watching this. No, probably not everybody is that that English Wikipedia peaks in terms of the number of active editors in early 2007 and other language Wikipedia's have peaked at different points in time. Although many around sort of that time. Many others are still like growing but our work has shown that the this pattern, which you may have seen, if you look at many language Wikipedia's have sort of this really rapid sort of linear growth that transitions really rapidly into periods of sort of slow decline is actually, you know, happening at different scales, but it's actually a very general pattern around big sort of successful communities. We see these very general kind of patterns. Now, you know, when we look at the early stages of communities we find they should come it's no surprise and I think it connects to a lot of the things people have already said in the panel that one important factor of sort of like healthy growth and the sort of like the communities that become big and successful tends to be openness, open to enable to involve contributions from lots of kinds of people communities with relatively lightweight forms of governments, governance make it off the make it's easier for them to get off the ground. As you suggested, there are a lot of, there seem to be trade offs between a lot of the kinds of things you know I hear a lot of people on the panel talking about like, on the one case sort of engagement and on the other case, sort of maintaining quality. And I think that some of our work has built on some work that other people have done to really unpack this dynamic set of trade offs between engaging more people in ways that I think are in line with what Jerome has mentioned and maintaining quality in ways that connect to what Christina and also Misha have spoken to. I think that part of the story seems to be that or this part of my story, I guess, is that this is not, it's not a story of sort of the spigot being turned off of the stream sort of slowing down. I mean, that's part of it, but there's also a big increase that we see in these communities that transition into periods of sort of maturity and decline. There are big increases in newcomer rejection which leads to decreases in newcomer retention. And this was shown first by Aaron Haffker and Stu Geiger and Jonathan Morgan I'm sure some subset of whom are watching this, or will later. And I think that that that we've shown that these dynamics also seem to play out across lots of large communities that communities succeed because they're open, or in part because they're open. But they become increasingly closed in ways that they mean that they do a less good job of integrating people who are showing up at the door over time. And this also seems to be a general pattern now. I think that that we tend to find that people that are early on in communities tend to consolidate power as their interests sort of diverge from the rest of the participants. So I think that this is a big question and this is something that that a lot of our sort of recent work in the Community Data Science Collective my sort of research group has been focusing on, and sort of understanding what's going on and our answer seems to be that there's that there really is this trade off that emerges over time between the initial sort of need to build a community to build sort of a stock of value to get lots of people in. There's a need that emerges over time to protect it from people that are showing up and have a desire to have things sort of look one way the benefits there, you know, business or point of view. And so you build a whole bunch of structure, really what you think of is like like governance structures which I think correspond, which might be ways of working or ways of talking which I think correspond to a lot of things that Misha and Jerome talks about, which are a number to maintain and manage quality, and the effects of that tends to be this real trade off and the ability to engage people, sometimes in ways that are that, you know, sort of like, just very systematically across different demographic groups, early on, when the stakes are lower, you can be more flexible. And so I'm really interested in the way in which these these those sort of dynamics begin to play out over time. And everyone on the panel. So I would like to give us all on the panel, maybe in three or four minutes to ask each other questions or if you have comments for each other, based on what you have heard. And then let's spend the last 10 minutes of it for the audience to ask any questions they may have I see that already Q is building. I think that Joe has already a question for Mako. So go ahead, Joe. Yeah. Just as a way to, to develop this conversation of this, I think, you know, everybody agrees to this to this trade off that needs to be solved. And it seems to be a very challenging, very challenging thing to do. And, you know, Mako, you know, you know a lot about all kinds of different weekies in different development stages and Wikipedia in particular, I'll be interested to know what you think about about the possibility of two tier systems, whereby we need to maintain the bureaucracy and and and and ensure quality on the one hand, but on the other hand, if we want to integrate more community members and get some low hanging fruits elsewhere. Get those people to contribute. We also need them to have fun, have an easy access be able to connect to each other. I know there's been some experiment about, you know, having sandbox, having articles pre validated by an experienced community member before they get in but that kind of kills kind of the fun, or, you know, your direct impact of it. What do you think about this being a potential way forward. So I love the idea of a two tiered system in a sandbox, in part because a little piece of Wikipedia history is that Wikipedia was originally created as the second tier sandbox for a different encyclopedia project called new pedia, which was going to be a big professional sort of a more professional sort of reviewed system, you built the open sandbox and in large part, I would argue because of its openness, it was able to succeed to the point that basically people sort of forgot about new pedia and it sort of fell apart. I think that the trade off is actually, I mean that's not a way of really answering your question. I think it's interesting. I think that in practice it's difficult to do. I do think that that Wikipedia and lots of other wikis exist in this broad ecosystem there are a huge proportion and no Isaac Johnson's on the call sort of has shown that like a huge proportion of people are consuming information from Wikipedia in places other than Wikipedia. And so there exists this broader ecosystem, which is already out there. I think that figuring out how to design those tiers and ensure that you have sort of participation in places where it's possible while also sort of like having it work another, having things sort of move easily between them can be really effective and I think that you've seen very successful examples of this and freedom of the software as well so I think that there's potential but I think that I think that it's it's it's hard to do. Thank you. Any other folks in the panel have comments for each other questions, maybe one or two more. So, I'll be much paid to this attention to this interesting discussion thinking actually what can platform do specifically to encourage participation by minorities or paper to highlight inclusiveness and highlighting on newcomers or something and I'd like to be optimistic but you know so far I'm going to talk about in a pessimistic note we are going to have a paper we have a paper that was accepted to I see WSM for this year that shows outcome of stack exchange efforts to try to encourage newcomers you know so they also had the same problem that there's been overall there's been attrition over a long period of time and you can basically newcomers don't feel welcome so they tried to one strategy they tried to implement is to have a newcomer badge you know so to highlight contributions by first time users to hopefully to make community more welcoming to them. So we actually tried to measure causal impact of this newcomer badge on behaviors. And this long story short is basically not not much impact it does not help at all it did decrease in the short term it did increase number of negative comments newcomers to see but overall it did not help stem the you know the rejection and the flow of new outflow of newcomers to the platform. So that makes me a little bit pessimistic but maybe I mean that doesn't say that there is no things that platforms can do but we need to try in the safety testing way maybe try many different approaches to try to find how we can actually make people feel more welcome and more contribute more widely. Christina one question for you is this study also taking into account the gender of the contributors or that information is not available. That information fortunately is not available. Yeah. So any other ideas people might have what you know platforms can do specifically because they do have a lot of power in control to highlight the flow top to top certain people, people's contributions. Maybe you know you know it might conflict a little bit with the governance structures but I think actually it is the duty of platforms to try to moderate some of the negative effects of social biases. Yeah and in the case of Wikipedia I wonder from the people who are in the panel or in the audience. This is relatively complex, given that the part of a lot of the governance model and policy setting and enforcement in Wikipedia is happening on the volunteer community front and then platform itself has limited, relatively limited control over what can be done in that case. If anyone on the panel has comments in this or in the audience that would be great to hear. And then maybe that can be the last comment on the panel and then we can switch to the audience questions. So having like encouraging badges not to like, you know, like showing like step or flow has done it in some cases successfully by adding badges to users to some of the kind of guide the community's response to it. I mean the paper I showed what I talked about it was like negative outcome of badges but there's could be some positive better ones so you're not directly controlling what maybe what people are doing but maybe by surfacing or highlighting some features of the community members you're actually trying to encourage certain types of behaviors to steer them towards the goal. So I have a lot of comments in this but also we have few minutes for audience questions so I'll keep those for now to myself and I'll follow up with you Christina. Isaac, if you can help us with the questions from the audience that would be great. We've got a couple. The first one is for Jerome comes from Maria in the chat and she says what exactly is the difference between direct and indirect when you say women hate competition at least indirectly. And how can it be seen or incorporated in the inclusion or exclusion from the real virtual dialogue. Yeah, so I'm not sure I actually said indirect. If I said that that was a mistake on my part. What I was saying was that apart from the concept of risk aversion and the concept of self confidence which is how much do you feel confidence in the case of Wikipedia that you can actually engage in an argument and eventually effectively and eventually win that argument or push your your your thesis. The taste for competition is about the structure of how you interact with people. So is it that you're in a group and there is one winner. Or is it that we're all together trying to achieve something. And you know, in theory, Wikipedia is theorized as the latter. This is a cooperation and deeper. But in effect, the newly experienced often very much looks like a competition. I'm going to win this over you. And you know, there are some features that are embedded in the platform that that kind of also create this. It could be possible to soften the interactions that editors have right from the start using clever social engineering. And I think that this impression doesn't come across that easily because this is really the factor that differentiates between, you know, successfully integrating newcomers, especially when it comes to women and minorities, as opposed to not. Great. The next question comes from Saif. I'm going to let you ask this unless you'd you'd like me to do it. Oh, thank you. Hello, can others hear me. Yep. Thank you very much and great panel. I'm really enjoying it. So my question is, it seems for instance, how do you guys feel that your cultural background might be acting as a gatekeeper for Wikipedia. For instance, you discussed that openness is very important for content production. But it might be that you're looking at this problem within cultures who value openness. And you might not you might have not yet studied how openness is currently affecting in cultures that have conflict with with with openness, who might become maybe they even freeze up when they have to work with with with others. And so I'm curious if you guys could talk about how you see that your own cultural background might be effect might be acting as gatekeepers for the results that we're finding on about content production in Wikipedia. And thank you very much. I'm really enjoying this panel. Whoever would like to take the question, please. So maybe I'll start. So just a interesting question one kind of anecdote that comes up. And my own work is, you know, when I started looking at this, when I started the project looking at ideological diversity and quality pages. I was, I had a result. I expected, namely that the more I expected more diversity to ruin the pages. And now and that kind of prior was based on this research that I had been seeing where I think it's still kind of showing up with, you know, like fake news and media diets are especially like bad and conservative like right wing circles and it's like all fake are shared by like, you know, there's all this research that I was kind of like consuming those pointing to like the conspiracy theory, like 2% are going to ruin collaboration and things like that. So that's what I planned on seeing in our data. And basically the opposite here. I mean, I don't want to completely oppose those as to like that we disprove that, you know, though that research or anything. But it's certainly like I just proved my own expectation and reliably showed that, you know, it was like a pattern we couldn't make go away statistically or in any way that like, the more diverse, I thought the first teams always created. You know, and so that's just, I think one example that and so we wrote the paper with what the data showed us it was not what I expected, maybe given my own perhaps my own culture bias in some way. So it's just one example where the data really drove, you know, the story. Thank you. Yeah, maybe maybe I can just add to that a little bit. I think that one great feature of Wikipedia is that it centers around languages. And so really, the amount of bias that you have to manage is defined within subcultures. So I think that, you know, cultural diversity is certainly relevant. But in a sense, it's tackled at a level that is relevant to each culture, not exactly so, because you know, languages don't really map onto cultures necessarily. I think that's one of the strengths of Wikipedia that allows to manage the amount and diversity of bias within a particular community as opposed to imposing a certain view on everybody. And so in this respect, I think that in general what Michelle saying, you know, I don't know if he has studied other other languages than English Wikipedia most of the results I know of for English Wikipedia but that should translate, relatively speaking to other languages as well. Thanks. The next questions from Jonathan moving through the queue. Thank you, Isaac. So I wanted to follow up on this, this idea of having two tiered systems, or just more broadly kind of having some amount of segregation, not necessarily in like, in all the bad senses of that term. But I think it's important between newcomers or peripheral participants and central participants. And I kind of two questions about this one is, is the purpose of these of the two tiered system to protect the newcomers from the community, or to protect the content quality from the newcomers. So it's important to consider that directly because I think that you would create different mechanisms. If your goal was to protect newcomers versus to protect content from newcomers. The second question is, if you're going to have some sort of two tiered system which of course we do have aspects of this and Wikipedia is for example English Wikipedia as a drafts name space, although interesting corollary the community is proposing getting rid of that drafts maker and I both worked on a project called Wikipedia adventure which created kind of a safe area for newcomers to edit. If you're going to have a two tiered system. How do you do with this problem of, or the potential problem of legitimacy. Somebody's coming into the community and making edits but they're not true edits, whether that's because they're in their user space or that's because they are only a kind of doing a certain number of things on articles, how do you make them feel like they're legitimate. And therefore, you know, bring them closer into the community. And then how do you also project to the community the core community that these are legitimate contributions from legitimate participants, and should be treated and evaluated So, two questions one, what's the role, what's the purpose of the two tiered system to protect community that are the newcomers or protect the content. And two, if, how do you make sure that if you're going to have a two tiered system that you preserve the legitimacy of the people who are participating in a multiple sense. And if I can ask the panel to respond, maybe in 30 seconds or a minute to this question, so that we can conclude, we can stick around and continue the conversation after the panel. Maybe I can say a couple of words but maker has not been talking I'm sure he can say things about this issue. There is no way I can answer those questions in 30 seconds. I'm not going to answer those questions. What I'm going to say though is that I think I think it's mostly about and you know this should not come across as negative I think this is mostly about protecting newcomers from the community. There is no sense of protecting newcomers from the type of interactions that established contributors tend to have, and the quasi legal arguments very dry legal arguments that they enter into, which are off putting and, you know, can be can be stressful for for a newcomer. And so I think this is this is this is what the purpose of this of a two tiered system should serve and also put back the fun into it a little bit. It's not about quality and this is something that you know, make or probably can say something in 30 seconds, having newcomers, his research suggests does not undermine the quality of a wiki. This is, you know, a recurring argument about whether you want to allow anonymous contribution so I will let him develop that point. So it's not an issue of quality to me it's really an issue of how do you interact with other contributors and how would you manage this dry self assertive bureaucracy to put a little bit of the fun back now. Definitely the challenge here is the issue of legitimacy as you pointed out, people just wonder stuff to go online they don't want to have many gatekeepers they don't want to have their stuff evaluated by, you know, 10 layers of authorities before they can actually see that they make a difference. So, you know, how do you get this equilibrium right is really the answer that I don't know and that we should collectively figure out. Make up any final remarks. Yeah, very, very quickly. I think that, so I'll answer the first question which is like, I mean, you can rephrase that question of what do we want do we want more participants or do we want like high quality stuff. And the answer of course is that we want both, like the wiki media foundation's mission is like literally part one, like we want to engage lots of people in production. And two, we want to create like a valuable valuable knowledge, sort of resource that lots of people can benefit from, for which quality is important. And the problem is that, although that there's a tension between those two. I think that figuring we can we can we can design better systems, which reduce the severity of the trade off that that are caused by our, you know, desire to navigate that tension, but I don't think that we're going to eliminate it and yeah the problem of course is always that we just, we want there are many wonderful things in the world and we want them all and sometimes it's hard to have them all. Thank you so much, Michael. Everyone on the panel. Thank you very much. Thanks for the time that you spent with us for the interactions. If you can go to this ether pad link that I just put in the chat there are a few questions from the audience that are not answered yet so if you can enter your responses there that would be great. And with that, I thank you and conclude the panel.


During 2011 through early 2012, proposed bills SOPA and PIPA were hotly debated in the networked public sphere in the form of thousands of articles written and exchanged online. Even with bipartisan support, both SOPA and PIPA were met with controversy. Some saw the proposed legislation as a means to end online piracy by stopping rogue actors. Others saw them as the potential end to the free internet. Ultimately, these proposed bills were dampened by an unprecedented public outcry carried out online. The following will explain three key moments of the debate through the mapping of the networked public sphere. On May 12, 2011, Senator Patrick Leahy introduced PIPA, the Protect Intellectual Property Act. PIPA was later accompanied in the House by Representative Lamar Smith-Sopa, the Stop Online Piracy Act, introduced on October 26, 2011. Both proposed legislations had bipartisan support, but as explanations of SOPA and PIPA circulated online, concerns were voiced by tech media, scholars, and the public. These dissenting voices gained traction in multiple ways, including the development of action-oriented websites specifically dedicated to defeating SOPA and PIPA. One result of these online action sites was the creation of American Censorship Day, held on November 16, 2011. On this day, internet users were prompted by many sites to contact their representatives in protest of the proposed legislation. This map, and the ones that follow, used circles to show how many links to online sources of information occur on a weekly basis. The larger the circle, the more links to that source. During the week of November 16, 2011, many sites promoted American Censorship Day and got attention for it. However, action-oriented sites were not the only response from the online community. In late December 2011, Reddit user Self-Prodigy issued a rallying cry to move domains from GoDaddy to other registrars in reaction to the company's support of SOPA. The calls circulated the social news site and gained momentum, resulting in a significant number of links to Reddit, as well as the creation of websites specifically dedicated to this boycott effort. Within hours, GoDaddy reversed its support of SOPA and publicized its new position. However, its previous position did not come without consequences, with loss of customers and negative press. From a single user, anti-SOPA sentiments spread through the sphere and had impact on businesses' bottom lines. On January 18, 2012, Wikipedia users organized a blackout day, shutting down access to the English-language Wikipedia site for 12 hours. Thousands of other websites also participated in the blackout, and many different sources were a part of the conversation, shown by the large number of circles on this map. On Wikipedia and other sites, a link to contact information for legislators encouraged action similar to that of American Censorship Day. On January 20, 2012, a mere eight days after the blackout, the Senate vote on PIPA was postponed. These three cases illuminate how the network public sphere can influence policy formation, political and social debates about the legislation of intellectual property continue. The patterns of interaction, web-based calls to action, and the wide variety of participant voices that make up the network public sphere may also continue to contribute to that deliberation.


When you think about your ear, you definitely think about, oh, hearing. Okay, we're done. See you later. Just kidding. There's definitely more to the ear than just hearing. The ear is involved in receiving perceptions of sound, but it's also involved in receiving perceptions of balance or position in space. And so let's look at the anatomy of the ear so that we can identify how exactly it works. First of all, your ear has three parts. Outer, that's what you can see. Middle, that's filled with air. And inner, that's filled with fluid. Let's make a note of that because that'll help us understand how it functions. Middle ear is filled with air. Really? There we go. And the outer ear is filled with air unless you jump in the water, in which case then it might get filled with water. This big leaf-like thing is called the pinna. Here's your ear canal. What is our technical term for that? The external auditory meatus, the external auditory meatus, the eem. The external auditory meatus travels all the way to what is this structure right here? That's your tympanic membrane. That's your eardrum. Eardrum is not good enough. Tympanic membrane. Your tympanic membrane separates the outer ear from the middle ear. Your tympanic membrane vibrates when sound waves hit it. Now, tongue was set up to receive chemical stimuli. Nose set up to receive chemical stimuli. Ear set up to receive mechanical stimuli. Sound waves. Sound waves, we're going to make them pink for fun because we have sound waves for fun, man. And they come in and they literally apply a force to your tympanic membrane. Your sound waves, most of the time, you can't actually feel sound waves. You can feel the air. But feel the mechanical vibrations that are making your sound when you talk. You can totally feel the vibrations here. And those vibrations come out.  Wow, that's really, like, intimate. A little disturbing, but we'll just stop right there. Your tympanic membrane starts wiggling. It's going to literally transfer the mechanical waves from the sound that's coming out into the middle ear. Now, if the waves just stopped, if we had just air between the tympanic membrane and our ultimate sensory structures, which are in the inner ear, then game over, you're not going to hear anything. Remove the structures in the middle ear, done. We got nothing going on. If we wiggle the tympanic membrane and we throw a bone like the Malleus into the mix, once we wiggle the tympanic membrane, what's going to happen to the Malleus that's touching it? It's going to wiggle, too. And then if the Malleus is touching, oh, you know, the Incas, what's going to happen to the Incas when the Malleus starts wiggling? It's going to start wiggling, too. And if they both know the Incas and are now touching who? My friend, Stapies. What's Stapies going to do? We got the wiggles going on. Now, this is crazy. Are you ready for this? Soundwave comes in at a magnitude of one. The mechanical movement has like a, I don't know, just I'm making up, just think of it as a size one. A size one earthquake. It's just tiny. After it wiggles the Malleus, the Incas, and the Stapies, your inner ear bones, I mean your middle ear bones, the size of the wave is going to be amplified 22 times. What? Okay, so that's like me coming in and going knock, knock, knock, that's a one. And what you hear when I go knock, knock, knock, 22 times louder than that. True story.  That's perfect. So what? Well, Stapies, who's Stapies touching? You guys, very important. Look, I'll make it green so you can see it. Stapies has its hands on another membrane called the oval window. The oval window and the tympanic membrane are the two structures that separate or that create the boundary of the middle ear that is filled with air. If a window membrane can vibrate the bones, which then vibrate a membrane, what is going to happen inside these fluid filled structures? Dude, have you guys been to the Discovery Museum? That's like my kids, well, back in the day when I was staying home with them all day long, we'd ride the bus to the Discovery Museum and we played with the water thing down there. And they had this like water wave maker and, oh, hours my children could stay and make waves at the water wave maker. And you push the, I don't know, it would walk in through the water and it would make this great big wave. When I'm yelling at you like this, because it's so exciting, the oval window is going to vibrate. And what did I tell you was true about the inner ear? It's filled with fluid. It's like a wave maker. We just transferred the anatomy of your ear. Dude, this is so freaking cool. We just transferred this mechanical sound waves into water waves inside your inner ear structures, which include your cochlea. Okay, we can't have it be that color because you can't see it. Let's try black. What novel idea? Cochlea. The cochlea looks like a little snail. And these guys have fluid filled in them also. Here are the semi-circular canals. Here comes the wave, a wave maker, because I'm yelling at you and you hear me. So the waves come in depending on how far the waves go up the cochlea, that will determine the pitch of the sound that you hear. So as I'm talking to you in all these different pitches and you can hear like all these different accents and incitements and whatever is happening, it's happening because the waves are like rushing in at different distances into the cochlea. All right, it's a mechanical wave. Guess what? You've got cells lining the cochlea. And I'll draw you on. It looks a little something like a look of this. Does this look kind of familiar? They're called hair cells. Hair cell and the hair cells synapse. Send messages to who do you think? Sensory neurons. Okay. Hair cell has its little twangers in the fluid. So when I'm yelling at you and the fluid wave comes in and the hair cell appendages, twangers, move. If they move this way, then the hair cell says, dude, send that message to the brain and fires a message to the brain. When they move back, it says, oh, there's nothing going on. Everything's normal. Really? And that's true. And that's how you hear it happening right now. What about these semicircular canals? The cochlea is where sound happens. The semicircular canals are involved in three-dimensional perception of position. So if you are on your side, now this is also just shockingly cool. Also fluid filled. However, the sound waves don't travel in this structure. Instead, true story, dogs, there are rocks in this thing. What? There are. And you know what? Those semicircular canals are set up. No, I have no idea because I'm spatially challenged. But I can visualize two axes, like an x and a y. And then I know that somewhere there's a z and then there's another axis that makes it three-dimensional. And those semicircular canals are set up to perceive all orientations of space. So if I knock my head over this way, they're set up so that the rocks in the tubes, right, are going to fall because of gravity in a specific orientation in those three tubes because of how they're set up in three-dimensional space. You totally understood that, didn't you? Because I'm so skilled at explaining three-dimensional things. But here I am with my head on my side because all the rocks fell in my ear.  I could probably fall asleep in this position because, well, you just will stay focused right now. The rocks just fell back. I totally have a perception of, oh, yeah, I moved. I'm standing back upright again. Oh, the rocks just fell. And they send the message for three-dimensional space. Now, they are fluid-filled. So if you get waves going through the fluid-filled semicircular canals, if you get it by spinning and then you stop, do the waves stop? No, which is why you feel like you're spinning still. When you are not spinning, you are not. Dude, the air is amazing. Now, we have some information coming in regarding balance, perception, three-dimensional, location, and space. We've got some information coming in regarding sound. Let's talk about how we're going to send that information to the brain and how we're actually going to process. Like, what's the pathway once we get into the brain? But first of all, pat your ears Dude, you guys are amazing.


What's going on everyone? Sam here with Leighton Sports Cards and I'm doing 2022 Panini Legacy Football Hobby 6 box break number 31 random teams. Here is the results to the random, leave them up for just a second. You can always find these under the results tab on the website as well as the link in chat. Good luck everyone. Just pull a dare to tear out of this. Why not? Why not us? David DeJabo to 100 Ravens got Trevor Lawrence Jaguars that is dare to tear right there. Yeah, dare to tear Trevor Lawrence Jags. Congrats Jacksonville. Eric R 58 out of 65 on the dare to tear. It's one of the case hits right there. Patriots. Congrats Eric. Nice one for you there buddy. Orange Breeze Hall that is 40 out of 149 jets on the rookie. Yeah, we'll mag that one. We'll mag the dare to tear for you. Base. Stick one here. RPA going to the Chiefs 51 out of 99 Skymore. There you go Kansas City that is Kurt D. Congrats Kurt. Said he was taking a couple of spots in legacy and boom hit himself a Skymore RPA. Congrats buddy. Red Dallas Clark to 299 Colts. Got many Leonard Fournette that is nine out of 20 for the Bucks. Trevor Lawrence, you think so? Nice. Thank you. Yeah, there's like a Kurt Warner one get pulled. I was like, yeah, not terrible, not great. Kobe Bryant, Chrome, the Seahawks. But it could be worse, could be better kind of thing, you know. There goes a mini. That is Bailey Zappy. 74 out of 125 for the Patriots. Base. Orange is going to be Darius Leonard to 199 Colts. Points here. That's going to be random to all at the end on the 250 points. DK Metcalf. We'll replace our other auto. Under the lights Josh Allen. Thank you Sean. Tyquan Fortin, not numbered on the Chrome Refractor for the Patriots. Last pack here. Red is Aaron Rodgers for the Packers to 299. Alright, that'll do it for box one. Good luck on box two here everyone. Probably not. Well, that's a second year. So I wouldn't want to be like, if it was a rookie, it's absolutely not. But if it's a second year, I'd be like, maybe I will. Bad. Fortin out of 50 is Terrell Davis. Not our decision to make Vaughn, unfortunately. I'm with you Vaughn. I would tear that one, I think. Yes, I would tear that one. Kenneth Walker, Chrome Rookie Seahawks. Vaughn with Terremol. Oh yeah. Psycho's. Drake London, 7 out of 35. Rookie Auto Falcons. There you go Atlanta, who's got them? Eric R again. Eric R doing well tonight. Nice Drake London. John Elwe? Let me guess Vaughn, you always bet the over on football games too. Desmond Ritter again for the Falcons. 25 out of 149. Vaughn's motto is life is too short to bet the under. Vaughn's motto right there. We've got Darren Waller. That is 16 out of 25 on the blue. There you go Raiders. I know, I'm always constantly sitting here cracking it. There is a mini David Ejabo for the Ravens. That is 40 out of 125. Congrats Ravens. Never mind, that's a good point. That's educated, that's educated. Educated for sure right there. Red is Strombatis, 72 out of 299. Vaughn I'm coming up for Christmas. Massachusetts. Nice. Got Debo Samuel. Silver, not numbered. RPA, Trey McBride, 9 out of 249. Going to the Cardinals. Congrats on that one. Arizona, Matthew C. Ace. Oh I'm Daxon Hill. Orange, AJ Brown. 139 out of 199. Yeah I gotta go up to Connecticut for Christmas too. I'm not going for Thanksgiving. Carson Strong on the Under the Lights Commanders. Now Eagles. Right, box three. Good luck here everyone. You're so blonde man. Guess not. Jordan Davis, the 299 Red. Yellow is Alec Pierce to 150. Mini, Bronze, Taysome Hill. That is 39 out of 75 for the Saints. What's up Belucis? How you doing dude? Worried about Vaughn. Yeah true, true, true. Got Jameson Williams, blue, 7 out of 20. There you go, Lions. Yeah if I'm up there Vaughn I'll come up to Mass for a day too. Because one of my buddies lives in Mass so. It depends where you're going. Nikobi Dean for the Eagles. I can get to Massachusetts in under an hour. Another 250 points. Random to all at the end. Yeah I'm like an hour away basically. Ooh, 0-2 in fantasy that's tough. Yeah me too it was just 0-3 actually. That was 1-1 so I can't really complain. Fred Taylor to 125, Jags. We'll also have Lamar score 75 points on me. Yeah, that's true. I'll have to come back for him. Isaiah Likely Chrome. Blue, Christian Watson out of 50 for the Packers. There you go, Pack. Go Pack, go. I have the same two quarterbacks to this one. Carinborough, wow. Kevon Tibodeau, Chrome. Oh definitely Vaughn. Definitely. So one thing I don't really get, we don't really get in Orlando. Got Destiny, Kenneth Walker on the short print. Congrats Seahawks. There you go, nice one there. I don't know but you just don't come around there though. No, yes it is very nice. I'm just saying like living directly in a city, you know what I mean? Kevon Tibodeau, that is ice, 11 out of 25 for the Giants. Because I love Orlando's food, I think they have great food. It's just like I'm not in Orlando all the time. It takes me like 20 minutes to get there. It's like if I want to go and get hammered I can't really... Yeah I just got it. Nice, Aiden Hutchinson on the RPA. That is 164 out of 199. I think I guess the points might have been extra. No, never mind. Jerry Rice. We should have all the cards out of this Jonathan. All cards shipped in this one. That's not necessarily the same for every break. Some are like inserts and hits and numbered and chrome stuff. Like Contenders is inserts and autos and numbered stuff only. But most stuff is all cards shipped. I would say, right? There's majority. I'm just going to Kobe that one. Not going to do it into the empty trash. Not going to be that guy. Evan Neal on the chrome rookie. Giants. Kyren Williams. Notre Dame, that is 54 out of 99. I believe he's Rams. Let me take a look really quick. Yes, L.A. Rams. Kyren Williams. There you go Rams. Nice hit there for you. Lower numbered RPI. Frank Tarkinson. Yes, that is Rams. Blue is Ronde Barber to 50. Clucks. Auto Tony Dorsett. One out of five. Look at that Dallas. Cory G. What a card man. That is sick. One out of three. Excuse me. One out of three on the Tony Dorsett. Hollow fame auto. Congrats Cowboys.  Legend. That's so nasty. Did not even know he was in this. Nice card. Congrats Cory. Tyler Batty or baby on the chrome rookie. Ravens. Got a Jamar Chase to 299. So we pulled a destiny. We pulled a dare to tear. We pulled a couple of really good. This has been a really good break. A legacy actually. Yeah, this did come out of nowhere. Odell Beckham for the Hs. Orange Alec Pierce. That is seven out of 149 Colts. That Tony Dorsett is so nasty. Yellow Devon Singletary to 150. For the bills. Got Minnie Johan Dotson under the lights not numbered. Minnie again this time Jerome Ford Browns. 37 out of 50. Under the lights Mac Jones Patriots. Demarcus Ware to 100 on the green Cowboys. Alright box five. Good luck again here everyone. Chrome Jalen Tolbert for the Cowboys. Red Darnell Mooney to 299 for the Bears. Rookie is Traylon Burks to 299. There you go Titans. Nice one there. Under the lights Cooper Cup Rams. Vaughn did you get your one of one auto yet? Got Kenny Pickett. 21 out of 25. There you go Steelers. Congrats who's got Pittsburgh. Lewis S. Heck yeah dude. Congrats on the picket. Mag that one for you buddy. That is big right there. 21 out of 25. Man what a break of legacy this has been. This has got to be. I mean I've only done like three six boxers. Obviously by far the best. Green Ricky Williams. Seven out of 100. Nah I just signed a card and sent it to him in person. Oh yeah. Oh yeah on your shelf. Heck yeah. Let's go dude. George Carlaftis. Bronze to 100. Chiefs. That's what I like to hear right there. Got to love some Harrison Bailey. Furious George. Yeah he is. Carlaftis is good. He loses. I know you're a chiefs guy. You must have been happy drafting him. Under the lights Jonathan Taylor blue. Six out of 20. There you go Colts. Three to 150. There you go Ravens. Nice one there Ravens. Is he looking good? Do you have any sacks this year? Yep. Taste of Hill. Silver. Not numbered. Those things on the mini. For the ages Joe Namath base. Chrome Andrew Booth. For the Vikings. And last pack we got something in here. We've got Kyle Hamilton. And 28 out of 99. There you go Ravens. Nicholas A. I am jealous. Very jealous of the Kyle Hamilton. Nice hit there buddy. And Rams Dool. Alright guys last box and we got a points random to do. Then we'll do a quick recap. And we'll be on our way to the next one. Chrome Jalen Tolber again. For the Cowboys. That's the second one we pulled to that. Granted those aren't super rare but. Base. Got something in here. And it is going to be. Devontae Price. That is. 176 out of 249. Devontae Price. He builds. Colts. Devontae Price is Colts. There you go. I always get him and Jalen Weidemeier mixed up. Who's on the Colts. So it's Devontae Price. Alright. There you go. Congrats Colts. Praying home some nice stuff there now. Vila Jones Jr. Orange Color Match to 149 Bears. Orange Kenneth Walker to 199 Seahawks. Red is Chris Carter to 299. There you go Vikings. Garrett Wilson on the Mini. And Peyton Manning. Got John Mechie the third. Going to the Texans. Under the Lights. 36 out of 60 on the Bronze. There you go Texans. Generations Matt Stafford Kurt Warner. Under the Lights Desmond Ritter. Aiden Hutchinson Lyons. Got Red. TJ Watt to 299. And Last Pack. Good luck here everyone. Blue George Pickens. 28 out of 50 Steelers. Nice one there. Alright guys. Going to do the random for both of these 250 points. Going to copy and paste all of the teams into a randomizer. And whatever two teams are on top at the end will get the points. Going to roll the dice. See how many times we're going to random. 6 and 4. Going 10 times. Good luck. 2, 3, 4, 5, 6, 7, 8, 9 and 10. Raiders Packers. Raiders Packers. Dual. And the Ravens. That is who is going to get the points. Alright guys. Going to do a quick recap here. John Metry III, Texans. Devante Price, RPA for the Colts. Kyle Hamilton, RPA Ravens. Wait. Yeah okay. And Kyren Williams, RPA for the Rams. They're both Notre Dame. It tripped me out for a second. Aiden Hutchinson Lyons, RPA. Trey McBride Cardinals, RPA. Drake London, Falcons Auto. There you go Falcons. Sky Moore for the Chiefs. Had a Kenny Pickett to 25 for the Steelers. 21 out of 25. Had a Tony Dorsett. 1 out of 3, Hall of Fame Auto. And Dared Attair, Trevor Lawrence to 65 for the Jags. That'll do it for the break everyone. Thanks again for the fill. Next up, Select H2.


So, today I am going to start with some basics of supervised learning. This is actually not online learning, but supervised learning that just as a precursor to our online learning ok. So, we will try to map what we are going to do in supervised learning in the offline setting, how the kind of metrics, the kind of algorithms, the kind of strategy we are going to see whether we will have a natural mapping when we move to the online learning settings. So, fine let us do the revisit our basic things that we would have done in the supervised learning methods. As I said in the beginning this course is about learning setups, learning different learning setups, looking for the best algorithms. When I say best, if I am saying something is best I need to show it like this is the best. So, how one is going to show something is best that you have to say that you cannot do better than this ok. So, our focus of this course will be like coming on with algorithm and then showing their best through giving a detailed analysis because of that as we grow on this as we go into this course we will get into lot of analysis. So, you have to be like with focus sometimes the proofs and all can get dry. So, if you lose track then you will be totally lost for the rest of the class. So, interrupt me at any point if you are not following anything. So, it is not necessary that you have to cover all the proofs, but till whatever steps we are going to cover in the group it is important that we are all in sync with it ok. So, what is our supervised learning method? What we have? So, basically supervised thing is you have let us say let us take the simplest case of classification further simplest case that is a binary classification you have given an instance you have to say whether it has a label 0 or 1. So, let us try to set up this simple thing in a bit more formal way. So, for this what are the things we know? First thing we are going to start with we are going to assign a label to something right. What is that we are going to assign a label? We are going to assign a label to let us say some instance. To be concrete let us take some example let us say you are a stock enthusiasts and you want to predict your stock is going to go up or down ok your share value is going to up or down. Then if I give you a stock that stock is what you are basically predicting the label right you are basically. So, that we are going to call the point from the set from which this stocks are coming we are going to let us say we are going to call it as some domain set and we are going to denote it by X and this is set of objects that we wish to label. So, in our stock example this script X could be a collection of stocks to which I want to say whether there we want to label whether their share price are going to go up or down. So, another example could be let us say you have some information about the weather, whether let us say you know let us say weather information has only two things like how humidity is and how what is the temperature. If I tell you this information you have to predict whether it is going to rain or not your job is to say whether it is going to rain or not. In that case this X is what in this in that case the X could be collection of all these pairs the pairs is like humidity value and the temperature value. So, in the in this case this X could be subset of R2. So, you give me any point in this that means somebody told today this is the humidity level and this is the temperature and you would like to tell whether it is going to rain or not. And second thing is label set, but the label set we have already fixing we have kind of already restricting ourselves to binary labels, but labels could be any number of labels. So, this could be like let us say set of outcomes and I am going to denote it by a script Y and in the binary case this guy can be simply 0 or it could be also I can equivalently take it as plus 1 and minus 1. And now the one who is going to give labels based on the input you give from this domain set let us call him a learner. So, learner is basically trying to do what he is coming up with a rule that will tell if this is the input this should be the output. And he may have access to multiple rules right it is there is like if you give me whether that let us say humidity and the temperature value I may combine them in some various ways to come up with the output that is saying that is going to rain or not. So, I have multiple possible rules right and the learner may have have access to finite set of rules and he may want to pick one from that to give you the outcome. So, we are going to that set of rules that that is accessible to the learner we are going to call it as hypothesis class and we are going to denote it as H and this H is nothing, but a collection of rules which are maps from Y to. So, you all of you understand this notation these are all functions which maps your input to some label in set Y. There could be multiple such hypothesis and all so that is why I am doing to denote it as a set this set is collection of these rules. Now, the learner here he has to find which is the rule is the best or like like you have to basically let us say for time being he has to pick one rule from this. So, how he is going to decide he is going to possibly decide based on the past observations or the past history. For example, he might have from historical data from weather department you would have seen that with this temperature with this humidity this happened rain happened or not. So, he may have all the data observed from past and he is going to use that to see which is the which rule or which hypothesis he likes to pick ok. So, he the learner has access to training data we are going to call it as training data and that is we are going to call it that is s and this s will have where we are going to say xi belongs to that we are all i to m and so there are m points we are going to call this training data there are m points in the training data each point here is a pair the first part denotes the object here that is coming from my set x that is my domain set and the second component is the label that is coming from my label set ok. So, we have these components in this the learner is have access to this training data he has access to this hypothesis class and now he has to figure out which is the best hypothesis for him that does a good job of prediction on a given data point ok. So, when I say data point it will be it can mean two things data point could be it will have this object as well as associated label sometimes data point I mean to say only the first part it may not have the associated label and in that case you have to come up with the label ok. So, we will either say simply data point or a sample when I say sample if we do not have label for it our job is to predict it when we once we have already have it that is part of our training data ok. Now, when I said the learner's objective is to pick and hypothesis from this that in some way does a good job right on the prediction task. So, let us try to quantify that like what is that the learner's objective would be. So, we are going to say here learning for the learner is we can hypothesis from this class which is good in some sense that we will make it clear in what sense ok. So, now, learning boils down to learning this hypothesis I want to identify which is a good hypothesis from this class which is given to me ok. Now, let us say now, I am learning on a data set right, but then the question is how is this data set looks like or how it gets generated right. Is this data is arbitrary like if I ask you somebody to give me data like can you just generate data and give it to me anything we will do. We are going to assume that there is an underlying process that is going to generate this data ok and actually that process is known to me and my job is to kind of figure out that underlying process that is generating data ok. So, for just for a beginning we will are going to assume a simple data generation model. We are going to assume that data is generated according to some distribution D. We said that the data points are coming from this domain set and how they are picked, they are picked according to this distribution D. So, is this clear to you? Let us say I have said I have a training data right. This training data when I said how are these points generated in this each of these points I had told you they belong to this, but when you generate this point it is going to be sampled from this set according to this distribution D ok. So, we have in this case we are going to say that Xi is according to this distribution D. So, this is about the first part of this data point how these samples are generated ok. Also this first part of this data point they are also called features ok and so, henceforth we start calling them as features and we are going to call this as domain set as also feature set ok. So, for example, in the rain prediction problem we said the humidity level and the temperature level right this could just act as a feature for us ok. So, given a feature I want to identify whether it is going to rain or not that is I want to assign a label and these features are generated according to this distribution D. We are going to say that this distribution D is fixed throughout it is not going to change, but I do not know it ok. What I get to see is the samples generated from this distribution D ok. This is one part of this data set right I told you how to generate the features and we are going to say that there is a label function f which will make it y i equals to f of x. Again I am assuming that this once you tell me feature the associated label is governed by this function f and we will assume again for simplicity for just in the beginning that these f's are again fixed, but I do not know them ok. So, how these features are generated and how the labels are generated both I do not know, but they are fixed. Now and we are going to usually say that this is the environment ok. The D together with f this constitute for us environment both of this I do not know, but the data generation is governed by this environment ok. So, now we are going to I am going to do a measure I am going to say that. So, we said our goal is to pick a good hypothesis from this hypothesis class right then we need to quantify what is that good we are talking about. So, that is the measure of success. So, the measure of success we are going to use is called if you let us say give me hypothesis h from this hypothesis class I am going to say its success is probability that h of x is not equals to. So, what does this tell? The probability that your hypothesis does not give the same label as the underlying label generating function f right and notice that I am taking this probability with respect to this distribution D ok. So, now this x is generated according to this distribution, but this is kind of any x it is it could be any x here right. So, whereas, when I did this training that these are the some specific data points which has been sampled from distribution D and that is made available to me. When you are trying to measure the success of the hypothesis you came I am going to look at all x's entire x, but I will be looking at by sampling them according to distribution and look over this ok. So, what we are basically do is whatever the training set that is given to me I am going to use this let us say and I will come up with some hypothesis and I want to measure the success of that hypothesis I am going to see how good it performed on a random x that I picked from some distribution D. So, when I pick a x randomly from this this that could act as my test point and these points are all my training points. Let us make this bit more formal. So, if I have decide defined my measure of success like this for a hypothesis H what kind of H you would like to use.  So, you have access to the hypothesis class and let us say I am a measurer like I am going to evaluate your performance and my evaluation performance criteria is this. What kind of H you would like to give me? The one minimizes this quantity right and we are going to call this guy as here. So, this is like prediction error can also call test error. As a learner you would like to find a hypothesis which has the smallest test error right. So, my goal could be minimize the hypothesis class H ok. Right now let us say we have not made any assumption that whether this hypothesis class is finite or infinite or countable infinite or uncountable anything, but let us let us be bit vague here and still write minimum of this quantity here. Ok fine. So, is this entire setup is clear now? What we want to what we usually try to do in the supervised learning case like under this simplified case like binary classification and everything ok. So, maybe let us why not to just write this x is a random variable. You understand probability with respect to random variable right this x is a random variable here and that x has distribution d that is what I mean that is ok. So, even though I have written this x 1, x 2 as small quantities, but these are like x when I draw right. So, maybe I should just write it here this x the feature vector is a random quantity that I am going to sample according to distribution d I am not sampling like somebody has sampled and given it to you and from that you have now got the realizations and then you are trying to do. So, think about think this setup like this. So, let us say nature has its own rule mother nature it kind of it relates whether the rain happens or not to humidity and temperature in some fashion. It follows its own laws we do not know about those laws, but we have observed this like maybe like for the past 50 years or something we have been observing what is happening when the humidity level was this temperature. So, let us say somebody has recorded this information. So, whatever the nature's law it has been using we have seen the samples of this over the last 50 years and that law is this that is the environment. It is generating this features and associated labels according to this distribution which we do not know, but what we are interested is suppose if I just get to observe humidity and and the temperature level will I be able to predict my rain the way the nature would have done it the way it would have happened in reality that is the goal here that is what I am trying. So, this is like it always happens that you will see the feature vector and at the end then you can think of label as the result. So, in this case whether rain happened or not is the label that you are going to see the result. So, let us say you saw both temperature and the humidity growing in some fashion and at the end of this as a result of these two the rain is going to happen or not. So, that is like an outcome like rain happened or not and that depends on these two values. You have observed let us say humidity and temperature and now you want to kind of predict whether it is going to happen or not and also like in the stock market case like you want to see tomorrow my stock price is going to share price is going to go up or not. From your so, market has its own dynamics a market laws are governed may be very complicated you do not know it. So, based on that you have passed historical data from the stock exchange you have observed that based on this like if this has happened like let us say whatever the feature vectors if this has happened the next day the share price went up or not. So, you have that passed observation and now let us say today something happened like you have observed the current market scenario and now you want to predict whether tomorrow your share price are going to go up or not. So, that is what we are trying to do. So, this is based on the samples generated by the underlying environment which I do not know and now I want to see that if any features are generated on this how good my predictions are h of x is what is basically prediction or the label assigning right. How good I am assigning a label that is as good as the nature would have assigned it if I can do this. So, suppose I find a h such that this guy this probability is very very small if this guy this probability is very very small that whatever the feature vector I am able to make the correct prediction of its label or its outcome right. That means, in a in a in a in a sense I have figured out what nature is doing that means I have learnt these things from my observations ok. Now, the next question is fine you have set up the problem you have set up your learning environment you have set up your measure of success how you go about achieving how you are going going to get a hypothesis from your observed data ok. So, all you have this somebody generated a data and give it to you they have been also you you let us say Lerner has said ok I am going to use this much hypothesis now this is your objective how you are going to go about it. So, basically I want to find a hypothesis that kind of minimizes this quantity right ok.  So, I am saying god told you these two things then what would have done in that case for any h can you compute this if you know the distribution if you know your function for any h you can compute this right then it simply boils down to minimizing this. But god why should god tell me right I do not know this what I have been like, but what I have been doing is I am observing what like what I call or god is doing and now I am trying to imitate him right. So, how best you can imitate. So, you have access to only this ok. So, from this you have to come up with something. So, how you how we will do this.


My name is Sasha Kruppnick. I'm a professor surgery chief of thoracic surgery in the surgical director of the lung transplant at University of Maryland School of Medicine. I came to the University of Maryland in 2020. Before that I did most of my training and most of my career at Washington University in St. Louis and spent a little bit of time at the University of Virginia in Charlottesville. Thoracic surgery is a great specialty. It's one of the few specialties left where you get to deal with a lot of different problems. So my practice, for example, involves lung transplantation, cancers, cancers of the esophagus, cancers of the lung, as well as benign disease of the esophagus in the stomach. So it provides a variety and I can do a lot of different services for my patients, some of which have problems in multiple areas. So after successful transplantation or resection or part of a multimodality treatment of lung cancer, patients get their life back from not having it and I think that's the most exciting part. I and my team are very dedicated to their care. So, you know, our approach is patient centric and what we realize is that not the treatments are necessarily cookie cutter standardized.  And the key is for surgeons and everybody else to present the whole kind of spectrum of care and let the patient decide on what they're comfortable with. And that's, I think, one of the advantages of a large medical center like University of Maryland. You have access to all the cutting edge trials. You have access to multiple physicians who are experts in their field. So I think that's an important thing for the patients to know that if they come here, I mean, you're going to get what's available now. The other exciting thing about the University of Maryland is that the research we're able to do. We're not just following the guidelines. We're setting the guidelines. We're advancing the care. The research that we do in the lab is literally translated into the clinic within a couple years. So we're able to do that at this institution because of the depth and the breadth and the resources that we have that a lot of other institutions, you know, on the East Coast or anywhere, do not have the ability to do.


If you're not moving your body in this way as well, you do then start to affect your posture, which will then transfer into increasing your risk of injury, or again, can even affect your immune system health because you see people with poor posture, they're all rounded, they start to get stiff backs and that can affect spinal health. And just think about all of the different kind of areas the immune system's trying to work and the spine and the neck and the head is a big area for our immune system. It wants to be able to move around, the fluid has to flow. So that's why I'm a big fan of mobility. And again, if you look back at things like Tai Chi or mobility routines that people have been doing, moving their neck and their shoulders and things like that, that's really important. If you enjoyed this video, please give it a like and subscribe below so you don't miss any future content. To learn more about CNM or its courses, head to www.naturopathy-uk.com.


Hey guys, it's Alex here from alexfigures.com and today I've got my mito red light mito mid That's a lot of mitos. I read light therapy device. I just received this. I'm gonna do a really quick unboxing We're gonna look at the specs the price do a quick comparison to the main competitors Jude being one of them and then I'm will fire it up see how it looks see how it performs and I will do a quick EMF test as well, so I'm gonna open this up now and Here we go. All right, so in the box we have a receipt Usage instructions, which is a A4 sheet not quite as fancy as the Jove products. We've got the specs on there and a few comparisons with the other one But it never lists. It's pretty simple how to use red light therapy. We've got some cables For hanging the unit on the wall, I'm assuming a power cable and then The unit itself So it's pretty solid unit middle casing. So yeah, it's good to see another middle Design a middle casing because a lot of the red light therapy for products Is there's a just cheap plastic low-powered LEDs there are two fans at the back as well. They look like pretty big fans as well So it's gonna be interesting to see how this goes when it's fired up Um, what do you need to know about this? This is the micro mid-light I've got the specs here 60 what sorry 100 LEDs Split between 660 nanometers and 850 nanometers, which is becoming that kind of standard for red light therapy devices You get in the red light plus the near for red light. That's the same as the jubelight as well beam angle of 60 degrees Which is great power radiance 100 MV is the main number you need to know That is our similar. It's not better than the due products. Maybe it's the same actually and it's much higher than a lot of the Other players in the red light space. So yeah, all good and well There is a switch on the top where the power power plug goes. You'll see two switches there upside down Anyway, one says read the other says in IR near and free So the thing is you can alternate between them and then we've got some brackets up the top here See those gold copper colored brackets to there and then there's four on the back one two three four So that's four these clips Well to clip into so you can hang it So obviously the ones on the top, you know, you can hang it on the wall or back your door or something the ones on the back Suppose what you can do is hang it above you like so Which would be good if you're using it for a theory growth. Anyway, let's fire it up. All right, we're all plugged in So let's see how this thing looks It's very red, which is great That's it just with the neofaradon You can hear and feel the two fans fans that bigger fans than the jub I believe Which actually makes a little bit quieter. Just like it's not as high pitch just good So obviously both off Red light on so you can't quite see But only half the LED's are glowing red the other half of the neofaradon I turned them on You don't really make much of a difference. The power radiance is really really high like one of the highest in the market I think they're even saying it's higher than juice. So anyway without having to meet I can't I can't test that. You know what I want to do now is test the emf levels when it's switched off on that 0.03 Micro Tesla's UT which is in the blue the green zone way down the bottom when I turn it on That jumps up to point zero eight To point one Which is still in the green zone though. It's flickering up into the yellow You probably can't see that still very low. That's at roughly the six inches out. I'm not doing this very precisely Yeah, if I bring it to about there rub to about point two So it's it's probably similar to the juice. Obviously if you go right next to it. We're in the red Let's test the electric field as well There's no Oh, there's very very little spike in the electric field when it turns on but It's very minor. It's still in the green zone. So I wouldn't be worried about that and that's right up so the takeaway from that is um, if You these are such high-powered units Same as the juice so this the recommended treatment area six inches from the panel If you're in that treatment area zone six inches will further out One you're getting all the light power you need because these are such high-powered unit You don't have to get right up to it and two if you're in that six inch stone You're in the safe zone for Non-native emf issues. So finally before I go we'll do a quick comparison with the Juve Juve doesn't have a unit this size. I guess the mini the Juve mini would be a comparable size This might a red light has a hundred LEDs. The Juve mini only has 60 This unit is much cheaper than the Juve It's retail that was on special at the moment for 450 the Juve mini is about six seven hundred bucks But I think this I don't know how long the special is going to last for if it's always on that price I'm not too sure Speaking of prices, I do over five percent discount code for my to read products the whole product range It's just simply my name Alex ALX just into that and you'll say five percent. Um, so yeah this on paper Is a much more cost-friendly option? It doesn't have this hot same high-end packaging and you know that the fancy Timer that the Juve products come with in fact the new Juve products have the built-in timer and they're bringing out the Bluetooth function for tracking your Treatment on on on your phone on the app This is I guess more of your run-of-the-mill budget mess market sort of product But if it does put out the power that they claim it does and I don't see any reason why it wouldn't then you know from Performance point of view. It's just as good if not better And in fact this actually has a hundred LEDs as well, but these things like flicker rate beam angle The true power that it's putting out all that sort of stuff that I want to look into when I can get a meter to test all this stuff So stay tuned to my YouTube channel and hit over to Alex first calm Subscribes my newsletter because when I do have that meter I'll be comparing as many red light products as I can get and I'll test every sort of metric and including emf and see what's the best Devices for your dollar. All right, that's all for now if you do want to buy one of these I hit over to my dough red light calm. I think it is and Yeah, use coded discount code Alex Alex That will save you five percent and I get a little bit of cut which will go into my fund for buying that $3,000 light meter. I'm talking about signing up. Bye