So, we're here at CS 2016 and you're the Cody team? We are, 4-0 anyway. So, what do you do? I'm Nate, I'm a community manager, project manager. I do a bunch of different stuff. Should I introduce everybody? Yeah, introduce those guys. When did he do it? He's a software developer. He does add-ons, Python stuff, all the add-on stuff you want. He's pretty good at it. Keith here is business developer guy for Cody. And over there is Martin Kaiser, who is the release manager and does a bunch of other stuff. So, how big is Cody? Software-wise or people? It's success and how many people? What's going on with Cody? That's a hard thing to come up with. It's really awesome. It's so fantastic. We're pretty sure, well, we know a few stats about users. We know that we're at about 3 million Google Play downloads or active Google Play users on Android. We know that in terms of overall users, it's kind of hard to get a guesstimate because basically what we're doing is we're counting downloads of add-ons and just guessing from there. But that number is somewhere around 10 million. And in terms of developers, there are about 35 to 50 really active developers. And then hundreds of people who just submit patches everywhere. So how do these 30 to 50 developers, how do they get paid? They don't. They don't get paid? Really? Is that okay? Yeah, well that's what open source is. This is how open source works. So it's open source free software. Yes, we're a non-profit organization and it's 100% volunteer ran. With that said, a few of our team members have been able to go get jobs in the industry working on Cody full-time. So we have a few that have done part-time contract jobs and we also have probably a half a dozen over the years that have had full-time employment based off Cody. But that has nothing to do with the organization. That's just a byproduct for them being good developers and for them working on stuff. And they work on stuff so much that commercial organizations have picked them up and wanted to use them in their own product. So these 30 to 50 guys are the top guys for our media player hardware acceleration, all that stuff. In the world? Well, and I mean I'm sure that they're the people who have been willing to donate their time for the cause of spreading open source software. Some of the guys work for Intel. Some work for the... Was that a secret? How does it work? We don't have any Intel guys but hey, if there's any Intel guys out there we would love some Intel guys. So my website is ARM devices. Yes. So you work on every ARM device? Yes. Modern ones. I mean there's a certain point where ARM chipsets are just so old that we don't run on them because we're primarily a native platform. So it takes a pretty significant, in terms of power, chipset to run Cody. We need open GLES 2.0 or higher, as a minimum. We run on the Raspberry Pi because of the hardware decoder that's in it. And so pretty much you need to have a decent hardware decoder. We work on basically any ARM chipset but honestly the GPU is typically more important than the ARM chipset itself because look at the Raspberry Pi. It has a very weak processor, right? But the GPU itself is fairly powerful and that's why you're able to do 1080p on it and you're able to do so much in Cody on it. But is there some issues with like some files just don't work on some devices because the code is not okay? Well so we do software decoding. So we always do software decoding fall back. So in ARM systems definitely you run into limitations with software decoding but with the latest, the last few revisions of ARM chipsets you can pretty much decode majority of the stuff in software. I've been specifically working on the Apple TV 4 port and their ARM dual core is so powerful that everything is done in software decoding. Everything from 1080p, MPEG 2, to all the H264 stuff. It's all software. If you want 4K smooths, everything, VP9, if you want H265... That pretty much has to be in hardware. 4K these days have to be in hardware right now, right? I'm sure in two or three years once if the ARM processors stay at the same getting faster and faster like they have been then maybe you can do that stuff in software. So why do people think Cody is so awesome? I really have no idea. I don't think it's about the openness of the platform and it's really customizable so you can do whatever you want with it. What do people do? Can you do everything? It's crazy what people do. Like every one of us probably has a different use case for it. There are developers on our team who only use it for live over-the-air TV because we have a live TV component where you hook up a TV tuner and it just works that way. There are people who literally only use it for music these days which I knew there was music in it but I've never used it for that. And then of course there are the people who do things like rip DVDs or stream... From what I understand Cody is evidently a great Twitch streamer for game viewers. I had no idea about this but suddenly somebody told me about this lately. And then a lot of people are doing home automation integration now and so one thing that I'd love to do at CES is go talk to some of the smart home guys just to see if they're interested in partnering with us directly on doing home automation stuff. We see videos every day of people doing stuff like putting Cody in their car putting Cody on pretty much any device that you possibly can. So I think a lot of it is the freedom and flexibility that we've built a platform that allows you as a user to be able to customize and do whatever you want with it. How's that possible? Because it's Android, right? And you do an app and you have an app in Android? So we do but it's not Android. It's native. It's native code Android. Yeah, so it's native code and then we happen to port to Android as well. So Android is just one of the many platforms you support. We run on every platform. Are you one of the most hardcore native code Android apps? Probably. Probably, yeah. Well we can't prove that, right? There might be some games out there that are better. We are the world's largest open source multimedia project in existence and we are most likely the largest open source multimedia project on Android and the largest native code project on Android. Most of the other ones are all closed source so we can't prove any of that. How does it work to do a native code Android app? It was weird. The very first build that we had on Android it was literally all C code plus one line of Java. And actually the first few builds were that way. Originally because of Bionic, if Bionic is Android's replacement of LibC we actually couldn't use the original NDK. We used a third-party NDK to actually do the compilation for the first, I'd say, year and then we finally got it compiling on the NDK once the NDK got up to speed and more stuff got integrated, more of the LibC calls got integrated into Bionic then we finally were able to actually do native compilations and now we've been doing it for a while. And the nice thing is I'm sure other people have copied the build system that we've done because we're all open source. And so the biggest thing that we want is we want to be on any platform that is possible. So everything that's possible out there. So that's why Android's there, that's why we even have iOS builds and you can side load it on iOS which of course is ARM as well. So Android is famous for these launchers and stuff. Could people kind of like do launches for Kodi and change the UI? Well, they can change the UI anyways, right? So you can always change this. So we have skins that you can change the UI as much as you want. It can make it look completely different. Yeah, we have probably at least 15 skins at any time but definitely a lot that are being in progress and being actively developed. But also you can use it as a launcher but the one thing that you lack is you lack the ability to have the settings exposed from Android. So the problem is as you've probably seen with other launchers you have the launcher settings and then you have the Android settings that's another screen. And what some people have done to overcompensate for that is add some of the Android settings into their own launcher. We will never do that because we won't write specific per platform functionality in our own setting screen. So you would have to have another... Essentially Android settings would become an app that you would run. So that's some of the complications with building a multi-platform program like ours. Couldn't you become an Android launcher and control more of the Android experience? So besides the settings, pretty much all the Android experience you could run. So you could run apps. You can, under programs on Android, you can go and you can launch apps and when you close that app it will load back up to Kodi. So it works as that today. But we won't put in the work to add settings into our settings for example because that will taint the experience of Kodi being multi-platform, right? And so our focus is never to be a launcher on Android. But that said, if a company came along, wrote a skin and wrote add-ons to do that we would not be against it in any way and they're more than welcome to do that. So you call it add-ons, right? People install the add-ons, it's not apps. Correct. So it's quite different from Android apps. Correct, right. What's different about it compared to Android apps? Well, two, I mean, there are two big things. Android apps are actual binary applications. They're, you know, compiled and all of that. Well, Kodi add-ons are just Python plugins. They're just a series of codes that you don't have to compile at any point that just run. So that's the big difference. Now, I don't know, one thing to make clear, Kodi can act as a launcher both for add-ons, for Kodi add-ons meant for Kodi, but it can also launch Android apps. It does launch Android apps. Yes, you just go to the programs and they're both listed there. All right. And once you launch Kodi, it's good at kind of like quieting everything else in the system so it uses all the performance. No, so like notifications, for example, we're never going to pull in Android notifications inside of Kodi. So you'll still get the notifications that come across the screen and it will look kind of conky, right? There definitely is stuff that third-party people can take and they can optimize that and they could build notifications inside of Kodi via an add-on, but we're personally never going to put that in court because that's not where our use cases and that's not what our interests are. And that's why stuff like Android TV doesn't have those, right? So do you think the ARM ecosystem is awesome? All these boxes are fantastic, right? Definitely, yes, obviously. You can buy cheap boxes and run Kodi and it's fantastic. Yeah, it's great. I just recently, so Raspberry Pi is a pretty famous ARM device, right? I just recently was playing with that $5 device of theirs that runs Kodi and it's like, this is ridiculous. $5 Kodi box. So what does that mean for the world? That means more people are going to watch more media on their TVs. Hopefully. That means Kodi however they want to and there shouldn't be things like cost limitations that associate with it, right? Now that we're at a point where Android's getting standardized enough to where they have standard APIs to do hardware decoding, we hope that anybody who has desire to run Kodi, no longer there's no barrier to entry anymore. What that means for the grand scheme of things, just that we have more users out there, us we're not going to change from our core mission, which is making the best home theater software that we can. And that's really what Kodi's about, is keeping that user experience as pure as possible and really dedicating it to the home theater experience. Beyond that, if we can bring that to everybody who desires that, that's fantastic. How about popcorn time and subcast? Is it possible to prioritize in Kodi people doing that? I'm worried fast, but I would like to just know, does it work? We can do whatever they want in Kodi, because Kodi's an open ecosystem. So there are add-ons for that. We have blocked all of that stuff on our form and we do not allow any of that stuff in our repo. And we do not encourage users to do that. Just like Windows, just like Linux in general, just like any open ecosystem, we allow the freedom of the user to be able to install anything they want. They just put a URL in it and it installs. You download a zip file that has a repository in it. So there's lots of stuff happening. And what's in the future? There's even more cool stuff happening. More 4K support, more hardware decoding support. One of the big features we hope will be a release sometimes this year is Retro Player, which is a console gaming built inside of Kodi. And so you'll be able to do things like pause and rewind your emulated game. Fantastic. Awesome. Every console, Dreamcast and Nintendo 64. Consoles that have open source emulators. Because again, we're completely open source, so we can only utilize what the open source ecosystem has given us. So if there's a Dreamcast emulator that works, we'll work hard to support that. Nice. Just one last thing. What's your favorite add-on? HD Home Run. HD Home Run. What does it do? So HD Home Run is a network TV tuner box and it allows you the ability to watch live TV in Kodi from any Kodi device. So all the Kodi devices in my house, all I have to do is load one piece of software and I can watch live TV on that regardless of where it's at in my network. For me, it's the Rooster Teeth add-on. Rooster Teeth is one of those online web video providers. I'm a big fan. Big fan. Cool. And you also have probably some favorites. Probably YouTube. YouTube? Yeah. Does it work nicely in Kodi? It plays the videos I want. It's not the same experience as you have on Android, but that's the downside of YouTube not letting us do it. Cool. Thanks a lot for doing this awesome work. I think there's a lot of fans out there. We hope so. Cool. So looking forward to what's happening in the future.


Hello everyone. Welcome to yet another session of our NPTEL on nonlinear and adaptive control. I am Shrikan Sukumar from Systems and Control IIT Bombay. So we are into the last legs of this course and we are in the middle of week number 11. So, well, I mean, in the background what you see now is a bunch of different kinds of applications that adaptive control has found using these are things like, you know, drones, fighter aircraft, spacecrafts, neural nets. So, so this is sort of the applications or at least one set of applications that I'm hoping that the algorithms that you've learned in this course will help you work with. I'm of course always fascinated by newer applications. I've seen quite a bit of automobile system applications also and I am always open ears to know what you're working on. Yeah. So please ping me and let me know what sort of things that you are using these adaptive control ideas for. And I mean I can say for sure it will be very interesting for me to learn. Now, what we are doing since the beginning of this week is the initial excitation based adaptive control. So, we already know, you know, the standard adaptive control for parameter learning requires the knowledge requires persistent presentation. Yeah, we already know this is more like an, you know, a condition on identification and not something that succeeds adaptive control itself and not something that can be easily gotten rid of. So, in order to make it a little bit more realistic several researchers worked on trying to relax this condition and one such relaxation is what we are looking at right now. And this is based on using only initial excitation instead of persistent excitation. So the idea being that only for an initial phase of time. We have sufficient excitation and beyond that you don't find. And so the question is then can you still, you know, achieve your, you know, parameter convergence and parameter learning in those cases. So this is the general idea. Now, we started with looking at a single integrator system. And this was, was rather interesting, I hope, we don't write it as a differential equation, but we write it in this regressor parameter form. And, and then we go on to look at development of these multiple layers of filters. And so we have two filter layers here. And so we have one filter layer, which is the YF, we talk about the implementability of this filter, and then connect filtered variables also just like we have a connection between the standard, the original variables, Y and U. And of course, then finally we design a second filter layer. Once we did the second filter layer design, we then started to address the tracking problem. Until that point, we didn't even really care about the tracking question itself. Right. So we, of course, designed this parameter update without any Lyapunov analysis at this point, because we are rather comfortable with the fact that there is nice negative terms appearing here. Yeah, which is unlike what you get in standard adaptive control loss.  We then, you know, choose our control also so the control is also already chosen. And so all we're left with now is to do the analysis. This, of course, we remember the reminiscent of the other noncertainty equivalence method we looked at, which was in connection with parameter projection. Yep. So I hope you can connect these dots sort of. So we do the analysis we of course have this dummy variable lambda which we use to dominate things again similar to what we had earlier. And finally we said that if there is this initial excitation condition, which was of course much weaker than the persistent excitation condition. We can show negative definiteness. Right. So we had these rather nice property. Right. I mean, and whenever you see something nice. I hope just like I do. I try to critique it and I try to figure out where the issues are or what is good. What is bad. Yeah, not just bad. I mean, of course, there's always there's a very, very powerful method. I've advocated it myself and I strongly recommend that you look at these authors other words because although there are other composite adaptive controllers which use similar ideas. In my opinion, these authors seem to have done a, you know, done it in a rather simple intuitive. Yep. All right. So, of course, we said that you don't have to verify the initial excitation for YF itself. It's sufficient to verify it for the original regressor Y. Yeah. Great. Now. A few comments that in order. So this is also what we saw first is that if there's no excitation, robustness is still an issue. If you haven't handled the robustness issue for that, you still need to do projection or Sigma modifications and things like that. Yeah. Second, of course, we showed how the IE condition is significantly weaker requirement. And of course, we also talked about the fact that the initial excitation condition is not uniform. Because the regressors typically almost always depend on the initial conditions because it depends on the state. So of course, it depends on the initial conditions. You need parameter dependent version of this YF. We looked at this in the integral Emma discussion a while ago and we talked about persistent excitation. But in this context also the same idea holds. Yeah. And finally, not finally, but we also looked at how or what would be a possible way of choosing. And we saw that this is not super easy sort of a nonlinear question. Yeah. The good thing for us is that such a lambda existence of such a lambda is significantly easier to show. And we don't care about actually choosing lambda because it doesn't appear in the control. All right. So the final thing we sort of mentioned and we discussed a little bit is that the behavior in the absence of excitation is not very easy. So this was one of the more key points I would say because if you look at the update law, you see that both these terms are very excitation dependent. The first term sort of depends on, you know, the YF transpose wire. So it's only the semi-definite. Right. So the definiteness of this, of course, is not guaranteed. And the definiteness of this is guaranteed if there is initial excitation. But if there is not, then there's also not guaranteed to be definite. So none of these are actually negative definite. Right. And so how will it perform? Right. Because if you look at the Lyapunov analysis, this entire Lyapunov argument is based on trying this term, trying to dominate this guy. So you try to break this into squares and then you use this guy to dominate this. All right. So our entire argument may be in jeopardy if you don't have initial excitation. Yeah. Now, what happens, how things are different in standard adaptive control is that there is neither of these two. Neither is there a negative definite term. So it's a bit sad. But there is also not a mixed term in the unknown. Yeah, because this mixed term essentially gets cancelled by using your standard adaptive term. Right. So of course one, I mean, I would say one sort of easy sort of way to deal with it. Right. One easy sort of way to deal with it. Let me see. Yeah. Okay. I was just wondering about the order here. So E is of course, so let me look at the order of things. This here is a scalar. Yeah. Z here belongs to R1 cross 2. And theta tilde belongs to R2 cross 1. Right or R2. Right. So now one of the things that I could have done, right, if I look at the analysis here from in V dot, I would. So I'm going to put this as an aside. Yeah. So that let me draw it a little bit more straight using my tool. I'm going to use a bit of an aside. So if you look at my V dot, my V dot is E E dot minus lambda theta tilde transpose theta at dot. All right. So now I already know what is E dot. So I substituted so minus E squared plus E Z theta tilde is what I will get from here.  Apologize new Y F while I have theta tilde. And suppose I have plus some additional term new at my disposal. All right. And if I do have this, what I can show here, right, what I can show here is that if I choose my new, let me choose my new as one over lambda Z transpose E, then I'm sort of fine. All right. So that's the idea. So what will happen if you make this kind of a choice of V is that if I now write this expression for V dot like this, if you see I have all I've done is plugged in for this V in here. And if I write this entire expression for V dot, I have this minus E squared from here and I have the two negative terms from here, right. And subsequently I will have this guy contributing to a term this one. And then this mixed term comes back here. All right. Now, if you notice, we've already mentioned that all terms in V dot are scalars. So we can take as many derivatives as we sorry, as many transposes as you want and nothing changes because the transpose of a scalar is the same scalar. So you can very easily take a transpose of this guy and E is of course a scalar. So E transpose is just E and you will see that this transpose of this exactly matches this term. So of course, these two terms cancel out. And what is this nice thing that we obtain because of this is that there is no mixed term anymore. There are only non-negative, sorry, non-positive terms in the V dot because this is non-positive, this is non-positive and this is non-positive. And so there is nothing to dominate. All right. So of course, what is the outcome of this? The theta tilde dot now has an additional term, right. And if you notice this additional term is essentially the certainty equivalence adaptive update law. Yeah. So this is exactly what you would have done if you were not using this initial excitation based law. So the initial excitation based law now has terms in addition to what you would do in the certainty equivalence based adaptive update law. Now, what happens because of this is that suppose there is no initial excitation either. Suppose our application is unfortunately such that there is no possibility of initial excitation, forget about persistent excitation. So what happens is that these terms are then not necessarily definite, right. So these terms are not necessarily definite. Now what that means is that you cannot really, you know, they don't really contribute to the Lyapunov analysis. So I might as well ignore them. Yeah. Because they are only negative semi-definite at best, right.  But essentially what you'll be able to obtain in the absence of excitation is that e goes to zero because of this term. Again, we don't repeat all this signal chasing analysis anymore. We've not done that for a while because I assume that all of you have seen enough of these same steps to understand which terms will actually go to zero. So I can very easily prove that this term will go to zero in the V dot. And you can of course prove that Y of theta tilde goes to zero. In fact, also Y of theta tilde goes to zero. You can prove both of these, right. So these may be some kind of attractive sets, right? Attractive invariant sets. If you remember, we talked about this attractive invariant set when we discussed this projection based adaptive control also. So similarly, you will also get some kind of an attractive invariant set in the absence of excitation. Yeah. But of course, we still end up proving that e goes to zero. And this is what we do in standard adaptive control anyway. Yeah, that we end up proving that the tracking errors go to zero. But of course, we cannot guarantee anything about the parameter estimation errors going to zero. So learning of the parameters is of course not guaranteed in the absence of any kind of excitation. But if you have even basic things, something as basic as initial excitation, then you are guaranteed to have parameter convergence. But in the absence of initial excitation, things don't go bad. Yeah, you still have bounded results. That is your e and theta tilde will be bounded because v dot is definitely semi-definite negative. And on top of it, you will also get the tracking errors to go to zero. So this last point that we made last time that behavior in the absence of excitation is not evident. This can sort of be resolved very easily by making this simple modification in the adaptive layer. So this new adaptive law, as you notice, contains a mix of your initial excitation based adaptation law and your standard certainty equivalence adaptation law. Yeah, so I hope you understand that we can combine the power of these two methods that have the initial excitation based law, which has these filters and then the standard certainty equivalence adaptive controller to get the best of both words. Yeah, in the absence of excitation, I still get nice tracking performance and bounded parameter errors. And in the presence of initial excitation, that is some excitation for some initial time, I get parameter convergence also. So this sort of last point is something we have resolved. Yeah, great. So sorry for that long initial preamble, but it was rather essential to understand better what initial excitation based adaptive laws are all about. So whenever there is a new paradigm, it's important to understand it well, to see what are the good things it has and then see what are the limitations also. So what we want to do now is to look at the combination of this back stepping based ideas that we have seen before with this initial excitation based adaptive law. Now, I really hope that you remember that this initial excitation based adaptive control design did not really rely on the dynamics so much. So one would hope that the dynamics itself should not really impact the initial excitation based design. Yeah, so but let's see how these two get combined. Yeah, that is the back stepping based ideas and the initial excitation based adaptive controllers. So the first thing to sort of notice is that we are still going to be looking at the matched uncertainty case only. So we are not considering the unmatched case here. So here we have the standard double integrator with unknowns A1 and A2 and the control in the same equation. So that is the matched case. And the objective is as usual to drive these errors even E2 to zero where even is X1 minus R and E2 is X2 minus R dot. All right. So before we, I apologize, before we go forward to doing any adaptive, sorry doing any control design, just like before we do this, we write the system in this regressor parameter. And what we do is we only look at this system because this is the one with the uncertainty. So we don't really care about writing this in any form. So we only look at this piece of dynamics and I'm writing this in this Y beta equal to U1. So, so if I take these guys to the left, I have this Y beta equal to U. As usual, I have this over parameterization here, right, because of this additional one that I put in here. And that's it. So and Y is of course X2 dot and minus X1 and minus X2. It's like that way. Now remember that as usual that this guy, like I should mark it properly, this guy is not measured. Okay. So this guy is not measured. This is pretty standard, right? Because what is this? If you think of this as a mechanical system, this would be like an acceleration term. So usually the assumption is that only the states are measured and not the derivatives and so on and so forth. That's the usual assumption. Yeah. Of course, you do have mechanical systems where you have accelerometers and acceleration is directly measured. But that's not very common. Okay. So, so X2 dot is not measured. Remember that. So this is exactly like the single integrator case where you did have one unmeasured quantity. But we did show that even with this unmeasured quantity, you can do this integration by parts and still implement the filter. Yeah. And so that's the same here. Right. So I define one layer of filters in just like before, which is the YF dot is minus sigma YF plus Y. And then I also define a filter on control because that's also an own quantity. So the known quantities in this equation are Y and U that define filters on boards. And of course, the filter gain bandwidth is exactly the same. And we take some, some, of course, that is some positive gain sigma and initial conditions are assumed to be zero. Yeah. So, so the important thing to remember is that again, we are not showing it here, but these are implementable. Okay. The filters are implementable. And with the same logic as before. Yeah. So there are two pieces in Y. Yeah. One piece contains these two, which are of course available through measurements. And then there is this guy, which is not available in measurements. So we just do an integration by parts. Yeah. To remove this. And that's it. And once we do integration by parts, it becomes implementable. All right. Great. The other thing to remember is that just like in the previous case, nothing has changed. I mean, only the definition of why has changed. The structure of the regressor parameter form has not changed. And this is the whole thing reason why this is sort of decoupled from the dynamics itself. So the dynamics has changed and that change is encoded in the Y itself inside the line, but the structure Y theta equal to U has not changed. Right. So that's what I would say that structure is. This dynamics. Agnostic. Yes, it doesn't really depend on the dynamics of the system. The structure is Y theta equal to U. This is the standard regressor parameter form. Yeah. And because the structure is not modified in the filtered variables, you will still have the equation.  So this still goes. Nothing has changed here. And this is nice. Right. Very cool. Okay. So those are the important points. Remember, I still write it in this regressor parameter form Y theta equal to U because of which I have U F is Y F theta. And I, you know, design the filters identically, though the dynamics has changed from single even the order of the dynamics has changed. The only thing is there is still again similar to the previous case of there is still one unmeasured quantity, but we deal with this using integration by parts. And so the filter Y F is still an implementable filter. All right. Excellent. Right. So now if you sort of look at what we did in the previous example in the case of single integrated, we add another filter layer. Right. So the first filter layer helps improve performance comes from slotting the original work in 80s, but it doesn't really help you alleviate the need for persistence of excitation. Yeah. So in order to do that, we need a second layer of filter. And that's what we did in the single integrated case and in the double integrator or if you have 100 integrators or if you have any kind of dynamics, you would do the exact same. Nothing changes. So the second layer filter is of this form. YIF dot is YF transpose YF. So I think this is also plus YF transpose YF with initial condition 0 and UIF dot is YF transpose UF with the initial condition 0 again. And of course, as before we have that YIF is positive semi-definite. Why? Because it starts at zero value, zero matrix now. And the derivative is a positive semi-definite matrix. Yeah. And therefore YIF dot is positive semi-definite. Therefore the way YIF propagates is also becomes a positive semi-definite matrix. Yeah. Excellent. Excellent. Now for UIF also you have YF transpose UF and the structures are again chosen smartly, carefully in what sense? In the sense that I get a similar looking filter equation, right? To the first, the original regressor parameter form translates to the first layer filter regressor parameter form and then to the second layer filter regressor parameter form. So the form does not change at all. Yeah. Although the dynamics have changed. Yeah. So as is evident, this entire change in dynamics is encapsulated inside this quantity. Now of course, also this quantity. All right. Excellent. Now again, we choose our update law that is theta tilde dot is minus theta cap dot as this minus Mu F YF transpose UF minus YF theta hat and minus Mu YF UIF minus YIF theta hat. The update law is also exactly the same. Yeah. Remember, we are not talking about the modification that we proposed here with the certainty equals adaptive law added to it. We're not doing that yet. Yeah. But this is the standard initial excitation based adaptive law. Yeah. So if you do this, if you take this in this way, you know that this UF minus YF theta cap is going to be equal to YF theta tilde and this guy by our standard regressor parameter forms in the filtered and the double filtered systems. And this becomes equal to YIF theta tilde. And once you substitute these here, you get minus Mu YF transpose YF theta tilde minus Mu YF YF theta tilde. And as you will, Mu F and Mu IF are some positive constants. Okay. So again, very standard. Oh, I'm sorry. This is already the fact that Mu F and Mu IF are positive is already mentioned here. I want to repeat it. All right. Excellent. And then we, of course, write a usual error dynamics. Yeah. Which gives me E1 dot is E2. Right. And E2 dot is X2 dot minus R double dot. Right. So this is A1 X1 plus A2 X2 plus U minus R double dot. So then we again write this as Z times theta. So this is where the Z shows up. You have the control and you have the R double dot. All right. So this is what you have for your dynamics. Excellent. Excellent. So what did we look at today?  More specifically, we looked at a small modification to the IE adaptive controller so that you add the C, the certainty equals adaptive control term to it, which ensures that even in the absence of excitation, the performance of the IE adaptive controller does not deteriorate. Yeah. So that was nice. And after that, we started looking at the back stepping based extension for our initial excitation adaptive controller. So we started looking at that. And of course we will continue on the same in the subsequent session. So I hope you're enjoying our discussion on this initial excitation based adaptive controller. And I hope to see you again from the next. Thank you.


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I am doing the random here from a previously break 2021 flawless football two box number 63 do on the random between the three teams here Colts of Peyton Manning Patriots with Tom Brady and Packers with Brett Farn on this triple two out of five again the top team after the random will take home this card good luck Colts Patriots and Packers here we go gonna roll the dice see how many times are gonna do the random eight times on the random again Colts path Packers whatever teams on top after the eight rounds we'll get the card good luck one two three four five six seven eights going to the Colts and Peyton Manning best thanks again everyone thanks for letting us know what you guys wanted to do with the card and congrats to the Colts


Welcome to another edition of RCE. Again, this is Brock Palin. You can find old episodes and subscribe in your favorite podcatcher at RCE-cast.com You can also find links to all of our Twitter's and blogs and stuff on there. Also on my blog specifically, my group at the University of Michigan is looking for a Linux person. So you can find our job posting off of my blog there. Otherwise, you can use the contact information RCE website to get ahold of me. If you may have someone who might be interested or you want to introduce to us or if you're interested yourself. Also, Jeff, I think you've got some stuff coming up. Yeah, we do speaking of hiring. My group is hiring as well. I'm in the server group at Cisco and we need some Linux kernel hackers for some network work. So either ping me or go look at my blog. I put a couple of details out there and let me know. We're always looking for good Linux kernel hackers.  The deadline for that I think is in. Well, it depends on when this goes. This podcast goes live. But the deadline I think is May 17th for paper. So get your papers in. We're going to Vienna this year. Be a good time. Vienna. Nice. So let's get into what we're talking about today. So today we're going to be talking about a group in an effort more so than there's actually no software involved with this. So we're going to be talking with two different people about data without borders and we'll let them explain it. But I have Nikki Rota, who's another graduate student here at the University of Michigan, as well as Jake Porway, who's one of the founders of the data without borders movement. So guys, why don't you take a moment to introduce yourselves? Hi, I'm Nikki and I'm a master student at the school of information here at the University of Michigan, studying human computer interaction and information analysis and retrieval. And I'm Jake Porway. I'm the founder and executive director of data without borders and was previously at the data scientists in the New York Times Research and Development Group. So what is this data without borders project and what are your goals? So data without borders is a nonprofit that connects expert data scientists with high impact social organizations so that they can better collect, analyze or visualize data in the service of humanity. Our goals are to connect data scientists who have extra capacity and want social outputs for that with social organizations who are doing good things on the ground and may have a lot of data or be influenced by data but don't have the capacity to look at it themselves. Now when you say capacity, what do you mean by that? Do you mean equipment? Do you mean expertise? All of the above? That's a great question. It's really all of the above and a lot of times even just the mindset of thinking about data. I think our long-term goals are actually to have a world where every social organization has enough data capacity in terms of thinking about data and planning out their projects as someone like Google or Amazon. We'd love it if it were as crazy for a mission-driven organization to plan a new project and not think about how they were going to use data as much as it would be for them to plan a new project and not think about a budget. So we're here thinking really about the mindset of realizing that data is important and then in addition to that the capacity that comes from needing people like resources, people who know what to do with that data, as well as a lot of the technology infrastructure. And we know that people in Silicon Valley and Wall Street have a lot of these things but the nonprofit and the social sector tends not to have the resources for that all the time. So is this like a commercial venture that's going to be funding itself or is this a nonprofit effort and how do you have this structured? That's a really interesting question. It is a nonprofit structure but we don't think that being a nonprofit means that you have to forever rely on grants and donations. So we have plans to make this financially sustainable and we're working through a number of avenues on that both from charging for services to licensing tools that we'll be creating as well as providing educational resources so that even groups that we can't work with directly could still learn from our webinars or from books that we put out on how they can think about data differently. So we are a nonprofit but we want to be sustainable as well. So let's talk about you specifically a little bit here. How did you originally get the motivation to do this and what was kind of your starting efforts to create data without borders? Well, as I mentioned, I was working at the time as a data scientist and I couldn't help but feel that we are at this really exciting time where there's so much data and so many tools to use data that you realize that very soon a lot of the world's decisions are going to be driven by data. And I was excited not just by that potential but all the excitement in the community around this. I'm sure a lot of your listeners and you guys have heard of hackathons. They sort of weekend events where people come and work on problems in their spare time. And I was so inspired by those. I went to my first hackathon. I was really kind of pumped up because I felt, wow, this is an amazing time where all these people are sitting in this room with the most amazing skills the world has ever seen. They're all great coders. They've got great machine learning skills and they're spending their spare time. This isn't work time. This is their weekends and their nights coming together to build something. This is really amazing. This is incredible. And I couldn't wait to see what we're going to come up with. And by the end of the day, the results that we came up with were like a little bit unfulfilling. There were a lot of mobile apps to help you find a restaurant. There were some programs people had come up with that were kind of like Twitter clones. I just thought, man, it's kind of tragic that we've got all this excitement in the community. And yet what we're coming up with are these sort of iPhone apps. And so I just put out an open call to my friends in the city and the data science community to say, hey, does anybody else feel this way? Does anyone else want to do something more socially conscious, find ways we can use our skills for good? And so it actually started as just a blog post amongst my friends saying, hey, what, you know, sign up on the email list. If people are interested, maybe we'll find some good data, maybe city data, maybe social data. And from that, we'll see if we can do something that's a little more socially positive. And I learned a lesson that day that you should never put anything online unless you want everyone to read it. Because somebody tweeted it and it sort of went viral. And before I knew it, the UN and the White House were calling and saying, hey, how do we sign up for this? And that was when I realized, oh, this is a potential to be something much bigger. Clearly people are interested. So we've been taking it forward ever since. So let me take a step back and ask you kind of a dumb question. What is a data scientist? What does that mean? It's a fantastic question and one that is hotly contested within the nerd community. It's become this new invoke term that kind of encompasses statisticians, data miners, data analysts of your, and tries to bring them under this umbrella now that there is so much data. So it really refers to people who are dealing with large amounts of data and trying to find insights from it and whether that's the guy who's programming the infrastructure to hold terabytes of data to the statistician on the other end who's actually mining that data to find patterns. And so the, while the definition is still up for debate, people tend to see it as kind of a mix of computer science. So being able to write code to get data and manipulate data and statistics. So being able to understand the, you know, the possible sources of bias and how you should deal with data and how to actually do some machine learning around it. As well as sort of a critical mind where the, I think, where the scientist term comes in, someone who can ask the right questions and drill down to that. So it's very much in flux, but basically kind of a coding stats, you know, numbers minded scientist looking for patterns in data is the way I see it. So Nicky, how did you get involved with this? Great, great question. When Jake sent out that blog post, I heard about it through some of the channels that I subscribed to. And believe it or not, I was actually in Michigan at the time when I heard about it. I was visiting the school for the first time and getting to know Ann Arbor. And immediately thought that this was something that I thought the school of information should get involved in. Somehow, somewhere another, there were people at my school who would want to join. So I met with some of our administrators here and some of the students who were on campus. And there seemed to be a ground swell of people who thought that this idea was awesome. And I said, great, I'm based in New York City. I'm going to contact Jake. I'm going to tell him that this is awesome. And that is exactly what I did. So the rest is history in a sense. We met up, I told him, and this was just when it was starting. Jake, we must have met in July or August of his past summer. I don't think we even held a single event yet, or we were just still building up and on. So yeah, Nicky was awesome and showed up and said, hey, this is great. I've got a ton of ideas. And we met up and it instantly clicked. And I basically said, you know, I'm at a school where this is what I'm intending to think about. I actually came to the school of information hoping to look at how nonprofits deal with information. That was specifically why I was coming to the school of information. I had, my background was working with various nonprofits in India in particular. And I saw what challenges they faced, not only meeting the reporting requirements that their donors had said, but also in terms of managing the finances of a nonprofit. When you've got funding from five different grants and these grants are all requiring that you spend the money in different ways, you can only buy one third of this employee using this grant and you can use the other two thirds of the employee using that grant. It seemed really confusing and I felt like this was a space where I could do a lot of good. So when Jake had this call for data scientists who were interested in helping nonprofits and I was starting at the school of information, the timing just seemed too perfect for me. So I wanted to get involved as soon as possible. Now, like Jake said, they hadn't actually held a data dive at that point. You guys were planning the data dive for October, the first one that you were holding in New York City. And I said, well, if you guys can't necessarily come to Ann Arbor, how would you feel about me putting or hosting one at our school? And Jake said, run with it, go, have fun. This is entirely the philosophy of our organization is to help people help others. And I got a lot of support from Data Without Borders when we put on the A2 data dive in February. And our constant conversations and talking about how to organize such an event really helped me and helped our organization develop on the ground here. Okay, so let's hold off on the data dives for a second. Let me jump back into something that you said a second ago that I didn't quite understand even when I was researching about this before the podcast. Little secret there, not supposed to research at a time, but I do. So I didn't realize I had always been thinking about this in terms of big data types of things that diving into publicly available databases and finding where groundwater is or whatever type of thing that nonprofits are geared towards for the betterment of humanity. But you were talking about even more mundane yet equally important types of things, reporting requirements. Is that a big problem for nonprofit organizations? Are they three to five people trying to do something and get bogged down with administrative? That's something that you can help with? Yes. So a great example that I have from my own experience is I was working with a nonprofit in rural India who was, they were trying to collect data on farmers that they were trying to help. And in these instances, their donors are wanting to know every aspect of these farmers live. So you have a community of maybe 20,000 families. And the donors are interested in knowing the demographics of the family. They want to know how much these families own. What are their various assets? How much do they spend on farming equipment? The level, the minutia of the detail that was required for this particular NGO to collect is mind-boggling to me because I hadn't had prior experience seeing the depth to which these nonprofits are required to create sort of transparency around the work that they do. So they have consultants to help them collect this information, but then processing it, analyzing it, knowing how to show the statistical significance of the impact that they're having. There's a lot of work currently on randomized control trials, particularly in the developing world. Statistical methods are being applied to how nonprofits have to think, how they show their impact, how they demonstrate whether or not they're actually doing the things that they say that they claim to be doing. And as we move to this more scientific approach to how nonprofits operate, the shifting skill set of what's necessary for a nonprofit to know how to implement, to know what data to collect even in order to demonstrate what work they're doing. Jake really hit a nerve when he thought of this idea is that actually there's a shifting need and data without borders is really trying to address these needs that are holistic in approach, not just analyzing the data, but even thinking about how and what types of data to collect. Okay, so that's fascinating to me. So this is not just your standard 501-3C mountain of paperwork that needs to be returned every year, but an additional level of reporting for nonprofits to be proving that they are doing what they're doing. I hadn't even considered or thought of that before in my own little narrow mind view. Let me ask you a trivial follow-up here. What's an NGO? You said that a couple of times in there. So a non-governmental organization. It's a word that kind of encompasses a lot of different types of institutions, but your traditional nonprofit generally. So back earlier in one of your answers, you mentioned something called a data dive. What is a data dive? Well, so a data dive is a weekend event similar to a hackathon where we bring data scientists together with three social organizations of community groups, nonprofits, who have data problems that are targeted but don't necessarily have the capacity to look at them. And we want by the end of this weekend for the outcome to be that these organizations have worked alongside data scientists, hopefully gotten some answers to the problems, whether that be a visualization, an analysis, even a consultation to say, hey, we couldn't quite answer this question, but next time if you collected this data, we might be able to, but alongside those results, have them think differently about data. So it's sort of a souped up hackathon that really focuses on collaboration and a wide range of outputs. So now do you have some kind of process for NGOs or nonprofits to go through to become eligible to attend these data dives? Like do they have to meet with you first and say, yes, we have data problems and you talk about it and determine that they actually do have data problems, or how does this all work? That's a great question. That's actually one of the things I think is one of the most powerful of the data dives. And that is that we have an open application process, but when we go to select the three organizations, we spend some time before the event talking with them about what their problems are, because a lot of times they don't know what they don't know. And so we get a data person alongside a social organization leader to iterate through with them where they say, hey, here's the problem that we want to solve and here's some data we've got. And the data scientists will say, okay, well, that's an interesting question, but you can't quite answer it with best data, but you could answer X, Y, or Z. Is that interesting? And the person on the other end would go, oh, that's it. I didn't know we could even do that. Yeah, Z is interesting. And what if we, and so in that process, they actually start to think through how you go from wanting to solve sort of a business question to formulating that into a data question and sort of exploring all the things they can answer. So we work with them ahead of time to go through that process, and that helps us narrow down a question, a set of data we think would be good. And of course, that's also necessary because we don't have a month, we just have a weekend. So we really look for targeted problems that we can hopefully chip away at a little bit in just about a day. Now, is this something that the organizations themselves, they bring their own data, so to speak, that ahead of time with these consultations and whatnot, you kind of counsel them and say, all right, for this weekend, we're going to need you to bring this database and that database and whatever other data source you've got or a BYOD event. Exactly. So a lot of organizations do have their own data and they know either they've collected it because of a project, but they haven't been able to look at it or they suspect that it's useful. So a lot of times organizations will bring their own data. However, we also realize there are huge numbers of open data sources that are becoming available. So governments and people like the World Bank are opening up this open data. So one of our jobs when we talk with people ahead of time is to help understand what other open data sources might answer their questions that they may not even be aware of. So it's a combination of the two. Some groups bring their own, some combine theirs with open data or some just use open data altogether. So it sounds like you're really describing in the commercial where we always talked about business intelligence and businesses have invested in this for a long time. You're basically bringing that to the nonprofit space. I think that's exactly right. And it's a good time for this. Not to say that nonprofits don't already have business intelligence built in. A lot of them have the resources for that. But I think what's changing is that the amount of data that's publicly available that could come to bear on these problems is vastly increasing. And because of that there are new opportunities for organizations that maybe didn't have business intelligence or weren't thinking about it quite the same way to bring in this new data to think differently. So for example you could imagine a data set from the census becoming available or about low income housing that a community organization that looks at bringing cheap affordable housing to people could take advantage of could learn more about their space. And so even if they have business intelligence about their practices or about their donations they may not have had this resource before. And it's a very new skill that a lot of them, a lot of organizations are still learning and that we hope to bring to them. Yeah so just like a business trying to be most efficient you're trying to make the efforts of the nonprofits be most efficient so they have the biggest impact with their resources. That's quite interesting. But some of these things I would think would take a lot more than just a weekend in these data dive things. Is there other things that Data Without Borders does to do long term? Yeah that's a really great question. So the data dives actually began as a proof of concept. When we originally started the idea we envisioned a global network of data scientists being able to respond to that that signal in the sky, any organization that needed data help. But we knew we couldn't really start there so the data dives actually were just meant to help us understand what the common problems were and whether people were interested. And they've been really great and actually have produced a lot of long term engagements beyond them themselves. But we also used them to transition to what we wanted to see which was these long term engagements. And so we have a group called the data core which is volunteers that work in their part time or weekends on volunteer and contract basis where they work for a couple of months with an organization and there they can really tackle bigger problems and they can iterate further. In addition to that we're also building out a fellowship model which would be a year long commitment from someone to actually embed with the organization full time. And in addition of course we have on staff data scientists who work on projects as well. So we actually have a range of engagements and we've been really excited to see that every time we try one like a data dive it sort of naturally leads to the next one already. So we've been really excited that people are really sort of naturally proving out this model for us. So Nikki could you give us a little detail about what you hosted at Michigan and how has that shaped both what you're doing there and what Jake is doing with Data Without Borders? So we tried to mimic the same sort of model of the data dive just with the scope being a little bit smaller. So Data Without Borders has been great. They've been able to pull in some really amazing and large organizations of ACLU I can think of the United Nations. We decided to focus basically just on Ann Arbor and Detroit based nonprofits to do a very similar type of event. We've brought in the African Health OER Network which is based out of the University of Michigan and we've also brought in Focus Hope which is a nonprofit based in Detroit. And what kind of things did they need answered? Is this something that you can talk about? Yes, and actually you can go visit our website and we've got several stuff up. Even on the Data Without Borders Wiki you can actually see the output from our event. The Data Without Borders Wiki was a great way to kind of demonstrate the flow and the process that our participants went through to actually create output for these nonprofits. But yes, the two groups had two very, very different question sets. Focus Hope was very interested in learning more about the participants who take advantage of the programs that they offer. They don't know much about how much overlap there is in participants and or where the participants are based in Detroit. And so they gave us lists of participants and we actually were able to map using GIS software where their participants were based using some open data sources from the census. And we were actually able to pull in some information about income levels and the various census tracks in Detroit to sort of help them get a better sense of what's going on in their community. And with the African Health OER Network this is a nonprofit that is actually providing open educational resources to universities across Africa. And they were interested in two questions both related to their network. The first was who in their network of doctors, of educators are participating and contributing to the network. Who is contributing materials, who is going to their conferences. And then the second question was which materials that they provided are being taken advantage of. So the videos that they've put online, who's viewing the videos? Where in the world are they viewing the videos? How often are they viewing the videos? So tracking the Google analytic data on some of the various resources that they've been available. And in both cases for both nonprofits we had some very interesting findings which was a lot of fun. So we're all a bunch of high-performance computing geeks, Jeff and I. And I was invited down to the A2 Data Dive and I gave a little talk showing high-performance visualization for massive data sets. What sorts of things can data crunchers like ourselves who are really just high-performance computing guys help out somebody like Data Without Borders? Something that I took away from the first Data Dive that I went to in New York back in October and then I saw again at the A2 Data Dive in February is anyone can contribute both in terms of your skill set but also the sharing that goes on between the participants. So Jake talked earlier about kind of the watershed moment when he realized there were so many people that were excited to volunteer their time. And we saw something similar at our event with how many folks, not just in the Umich community but also in the Ann Arbor community were excited to share their skill sets. So we had folks who were really great at network analysis. We had other folks who were great at doing geo-processing. And in this space, with all of these different heads coming together to face a problem, there was a lot of sharing of information, of toolkits, of skill sets. So it doesn't much matter what exactly you bring to the table. It's being part of the conversation and the dialogue that can help yield and bubble up these really interesting solutions to the problems. Yeah, I think Nicky really hit the nail on the head there.  And in addition, even though this is a conversation primarily about data without borders, you know, we'd be remiss not to mention that there are similar efforts going on in different spaces where people could use their skills as well. There's sort of this new civic coding or kind of hacker activism growing up in groups like Code for America trying to bring together developers to help with local communities, especially through government. There's also Scraper Wiki. So if you're not familiar with Scraper Wiki, they basically have a platform where people can say, hey, I found this data source online, but I don't know how to read it. It's not machine-readable.  And then developers can sign up to solve that problem. Actually, you can write a scraper in PHP or Python that will deliver that data in machine-readable format that the other person can then download or visualize. So there's sort of this growing movement of ways that people can use their coding skills for the greater good. So let me ask you a question on a slightly different tack here. Being a data scientist yourself, you must also value data, obviously. And so therefore you must also be gathering data on yourself, on data without borders, and have some success metrics and things like that. What kind of things are you tracking about yourself? Do you track about the long-term viability of the answers that you provide to people and are they able to sustain that and how well they do with the answers and the knowledge that they gain? And what are the kinds of things you look into? That is such a good question. And while we do have analytics around our day-to-day functioning, how many people are signed up, how many projects are active, what's volunteer retention, a lot of our impact is measured based on how we help others have impact. So like you said, a lot of the indicators that we're actually looking at, and what I said looking at, we're young, so we're actually just beginning to collect these, but are on exactly the questions you asked.  How can we measure their impact? I mean really if a lot of them are trying to do is use data to show that they're doing their mission better, then we show that we're doing our mission well by showing that we can show that they did their mission better. So a lot of the metrics that we try to figure out early on are the kinds of metrics that would show that they had an impact. And I think Nikki gave some really great examples of organizations thinking through those problems and how that's a very tricky game to play. Jake, more than the last data dive you guys did in DC, weren't those some of the questions too that the nonprofits were asking That's a fabulous point. Thank you very much for bringing that up. So Nikki, ever the rock star, was at our DC event, so not only holding her own events in Michigan, but traveling across the country to come help data do-gooders in need. And she saw there, she was referring to a group called the National Environmental Education Foundation, and basically they try to increase environmental awareness around the country, and they basically do that by telling newscasters or healthcare providers information they should be sharing with their constituents. And the big question for them was, how do we know that our message is actually getting out? And more importantly, how do we know people are doing anything about it? And they didn't really know where to begin, and so we spent the weekend, the volunteers came up with a strategy for a number of different data sources that they may not have thought of before that could help them understand that problem better. So there it was not only, let's try to help them with the data problem that we can then measure the impact of, the problem itself was, how do we measure this very difficult impact question? I think we gave some really good answers to that. So Nikki, so we did this A2 data dive this last year, and are we going to be having any more of these going forward? Oh, yes. We're excitedly getting ready for next year. Obviously, you know, all of us are winding down in the semester over here, but we've already got a formula for how we want to structure fall semester and spring semester. And the idea is actually to start bringing in the nonprofits over the fall semester for what we're calling data jams, like small afternoon, maybe two or three hours information sessions so that the students and the UMish community can get introduced to them and their data, but then also provide that space for these nonprofits in the Ann Arbor area to actually think about the data they might want to collect for our event in February. The issue that we found when we held the event in this past February is that a lot of the nonprofits in our community are small enough that data might not be actually a primary goal of a lot of these institutions. And so helping them think through these problems of what information is important for me to collect in order to answer the questions that are pressing for me is actually part of the service that the A2 Data Dive can actually provide to these groups. So just curious, you said next year, you mean next academic year starting in the fall, right? Yeah, we already have the dates set. We're probably going to be doing it on the weekend of February 9th, 2013 is when we're hoping to have the next event. Okay, here's another kind of off the wall question. If you had to characterize what do nonprofits need? What do they ask you? Are there two or three kind of common questions that you get asked from these various NGOs? Yeah, definitely.  But in particular, some of the smaller goals in answering that they ask for a lot are especially visualization tools. I don't say, oh my God, I'd love to visualize my data. Put it on a map, give me a dashboard. I just want to see what's going on. The big problem with data is it comes in this big opaque brick. People can't understand it. We're very visual creatures. So visualization is really one of the big ones. I think another one really is just understanding what data to collect and really knowing what's out there. So they have some sense of what data they have and they kind of know about what other data is out there. But we just want someone to verify that they're doing it right or open up their eyes to what other data could be useful and how it could be used. I think those are really, by far, the two biggest ones that we see and then from there it breaks down along specific business questions usually really to trying to find some kind of predictive analytics around a program that they're doing or just trying to understand the general statistics of what is actually going on in their programs. So what are some of your goals for data without borders going forward? We asked Nikki about what we're doing here at Michigan. What about the national organizations? That's a good question. So I think we're going to continue working on this chain of long-term engagements that I was mentioning before. So continuing the data dives, building out our data core of our volunteer staff and working on fellowships and hiring more people on staff so that we can just get more people involved and doing good things. In addition, we think that a lot of this, we've been able to tackle a lot of problems from the data perspective with social organizations, but there are a lot of other voices that could really be a part of this. So we realize that we don't necessarily have development skills. Once you build that dashboard from the weekend, who builds the software that keeps it running or the content management system that runs behind it? And who designed it to be usable? So we realize that far beyond data, we need to start talking to developers, designers, getting other communities involved. So how do you get the word out about this? How do you recruit for data dives and things like that? You talked about the watershed blog entry at the beginning of time, so to speak, but how are you spreading the word and spreading the excitement? Like have you ever been flashed on it? Or how do you appeal to the mass geek audience to get people to help? So I'll let Nicky answer as well on this, how she found this experience. But we sort of got lucky early on that so many people are excited about this idea. It didn't really take much to get it to spread. So we've been very lucky in having a lot of support from big networks like the United Nations, PopTech, the O'Reilly publishing group, and groups like that who have really supported in getting the word out, and we've mostly just been fortunate in connecting to the right people. In addition, we know our own, so we often hit up meetup groups and other data hackathons to spread the word. But the geeks are a tight-knit community and word travels fast, and so far we've just been putting it out there and it's been spreading on its own. So let me interject actually. Have you actually ever interacted with any of the maker organizations? Because I'm in Louisville, Kentucky, and there's a maker space here and an open-source organization, and it seems like a lot of the ethos that you guys are talking about are very similar in values to all the people who attend the maker meetings and whatnot. That's a really great point, and we haven't actually directly reached out to the maker community yet. Certainly some hacker groups, but mostly on the development side, the software development side. And so it's a great point and another great community to mix in, but we've mostly been talking to sort of the data software side. Yeah, I guess I should clarify. The maker group here in Louisville, there's a strong software side to it, and that's why I was saying you don't necessarily need to build a better bread box to hold questions, but there are software components to these guys. That's a great call, and it's conversations like these that get us to spread out further on those networks, so where should someone check out to be able to set up their own data dive in their location? That's an awesome question, and I'd love to let Nicky speak to this after this answer, but we have sort of a protocol, a best practice that we're very much working through, and Nicky was so awesome in helping us draft and really work through our first remote engagement with somebody who was passionate about doing this, which is datawithoutborders.cc, which is our website. You can sign up to get involved through our Get Involved page, but in addition, if you are really interested in running one of these yourself, please reach out to us directly. We have an email contact at datawithoutborders.cc, which we use for people who are interested in that kind of engagement, so if it sounds fun and something you want to bring to your town and your local communities, you can contact us at datawithoutborders.cc at datawithoutborders.cc. So, again, this is about the documentation that datawithoutborders had created about best practices. That document was instrumental for us in terms of making sure that we had taken appropriate steps to prepare for what kind of outcome we would get, because, again, like Jake was saying, this data-dive concept of knowing the mechanics of what would be useful for us to implement what processes we should have in place, making sure that we had contacts on the A2 data-dive side with the nonprofits, working them through, making sure their questions and their data was prepared was instrumental in pulling off the event. I'd also like to follow up on the point about civic engagement and engaging the community, which is a great space for some of these hacker groups and for practitioners of data science in their work, but also as a hobby. And we contacted All Hands Active. There's the American Statistical Association of Ann Arbor. And what was amazing to us was we had assumed that because it was a University of Michigan event that we would get mostly students, members who stepped up and said, this is an amazing idea. I'm so excited to contribute something that I love to do good in this world. And so having Jake and having Data Without Borders to kind of walk us through making sure that we were prepared for this event, but at the same time reaching out to the community and seeing how many people were excited to participate was very heartening to us. Okay, Jake and Nicky, thank you very much for your time. We'll have this up soon. Great, thank you. Thanks guys.


Could dreams be us visiting our parallel timelines? Timelines are converging right now into the greatest and highest good of whatever timeline it is. These timelines, as they converge, little things are changing. And that's how we get the Mandela effect. So could our dreams be us visiting our parallel timelines? Heck yeah. Here's a question for you guys listening right now. Have you ever felt like your dreams are more real than your real life? Yeah. Big time. It seems like your dreams are, you're more in touch with reality. And when you dream, you never really know what time it is. You're outside of time. So when you're having those dreams, you're able to transcend time. Time is a construct of the third dimension. But time is only relevant to the planet we live on. On Mars, an average day is, I think, 28 or 29 hours. Right now, I'm 56, but on Mars, I might only be 47.  I do think that our dreams could be us visiting ourselves on parallel timelines.


Oh, we're gonna go Hey, welcome back to our stupid erection to get some Corbin this is red and we are Currently waiting for the family men season two. We've never done this tour We react right when it goes so we'll see how this goes and I think I see cc's on right your cc's are on right oh Yes, they are okay, and it's like 15 seconds, but we're about to see the new family men season two trailer Live with everybody else. So if you're in the car, I think there's currently 25,000 people Watching so lion you can't even read the comments. It's all right by two seconds and hopefully this has subs if it doesn't we're gonna stop it Okay, good Which you are grateful to I Check it just chicken Mission economy ready to go Naira G You can become knowledgeable from others knowledge, but you cannot become wise from others wisdom Did you see our uh the um the one we thought we knew from family man but it was she's in season two yeah yeah yeah I was I was probably even though we were counting down at the same time my connection I was I'm sure a good five six seconds behind you oh really oh I'm sorry yeah yeah yeah so you you were laughing at stuff five seconds ahead and then somebody getting shot in the head oh that makes sense actually for me in five months yeah so if any of my responses were reserved it was because I didn't want to distract from where I knew you were way ahead of the game gotcha uh looked good that this show is very interesting because it it unlike Mizzapur which did have some comedy this literally sometimes was very comedic at points in the first season like especially with the family like uh him and the kids and their relation and obviously in this one um him and his wife are obviously going through therapy because you know uh it's a pretty big lie that uh that he was in there exactly and I think they did exactly what fans of the show were expecting where you get exactly the two halves that most episodes are you get the family aspect that's just like this dramedy situational thing with the family and then you get the crime espionage thriller part so it and I like what seems to be this new villain that's part of the show now yeah uh it definitely seemed um it doesn't seem like it's caring over it all almost yeah it seems like it's starting off with something new yeah so like obviously at the end of last there was some questions but there wasn't a ton but we didn't know if they were gonna carry it over into the next season this exact maybe they will but that's not that's not the vibe I got not at all they might and they might do something really simple what they clearly are carrying over is the personal relationship stories uh and the family relationship stories but as far as like the actual things he's trying to solve and fight it seems like we're gonna get either something completely new or something just evolving into something much bigger than what they were doing before it's what I was hoping for absolutely um and you uh I was trying to remember her name of who we it was she's from Rajasthanalism right that's that's who we thought we knew from family man but she's only in this one she's one of the the main lead from Rajasthanalism Samantha do you remember why I'm blanking yeah I'm sorry I don't know why I'm blanking you remember Rajasthanalism right oh yeah yeah the the main girl that we really liked from that yeah yeah she's she's in this how did I know pick up well I don't know time I guess that's not too uncommon but no no oh you I'm not frozen over here can you see me can you hear me well rick can you hear me rick well you we're here but rick is not hold on I could see you the entire time oh you froze and it said connection was lost and so I was just I was like well we're here and you were just looking off to the side I was like oh god I hope he just doesn't take off his pants for some reason what made you think I was wearing pants Gordon that that's true you have no idea what we're wearing in these little Skype reactions that we do but I feel like I'm broadcasting straight from Bella Legosi's house uh I'm excited about this obviously it's the same creators uh who obviously I thought did a really good job with the first season and obviously Mono is gonna not get out of the park of course uh that's that's not even a question um and so it's just we had to wait so long I'm hoping they didn't change very much from their original vision because obviously we know they they initially pushed it back I don't know if it's for to do reshoots or if it was just so like some time dissipated from that event that went on with that other show that people got upset about I'm hoping it was just they wanted to kind of like leave some time and it wasn't we're gonna reshoot this to not offend people I don't know what they did that's just my hope because I never I never want people to change what they do so they don't offend people it's not not my cup of tea I'm with you but yeah uh I don't I don't know what that is but uh it's it's exciting I think I'm hoping Amazon I think they would it did look bigger didn't it it did look bigger because obviously this was right at the beginning with Mizzapur and this and and and all these OTT platforms first came out and so they were still trying to testing it out now that it's worked with Sacred Games and Mizzapur and and all these other shows I'm assuming they're just gonna keep pouring money into it because it it works absolutely it works and I think they absolutely should and this is typical for a lot of shows like it's funny when you watch a Game of Thrones season one and two you go back and you realize oh wow they did not have a lot of money and then the show became a hit and they're like what do you need 25 million an episode sure well where's the check yeah they had no problems cutting that check for the last couple seasons and they still didn't give a shit about the story anyway still better when when is when's the drop date do we have an air date for episode one June 4th June 4th so we will be there we we should have already gotten to another series but we just we just haven't yet so this is our first one of this year so we need to get a lot more before the end of the year at least I like to have at least five series before end of the year yeah this is the longest we've probably gone without watching a series but obviously a lot of things had to shut down production yeah but hopefully well we still got a lot more obviously this and then we do need to watch scam we just probably won't be able to do an episode by episode reaction on youtube and then uh and then um made in heaven which is an amazon seer zoe octar directed that one um so there's a bunch that we need to get to and we'll get to him before the end of the year because I like to do at least five at least so we have like for our award so we have at least five to choose from but we're halfway through the end we haven't watched one so uh we need to get on that so let us know what you thought about the trailer uh down below and uh let us know why we're idiots as you always do down below