Sounds good. All right. Well, welcome everyone to the Community College Consortium for Open Educational Resources Fall 2019 webinar on Copyright and Licensing with OER. I'm Matthew Bloom. I'm the OER Coordinator at the Maricopa County Community College District in the Phoenix, Arizona area. And I'm also one of the Vice Presidents for Professional Development with CCCOER, and I'll be moderating a panel discussion today. So I will introduce our panelists, and then we will talk a little bit very briefly about who we are at the Community College Consortium for Open Educational Resources. And then, Gen. Wetzler from Creative Commons will provide a flash review of the different Creative Commons licenses. And then we'll have a panel discussion, which we hope to be somewhat interesting and avoiding some of the two complicated discussions, but actually at the same time engaging in some of the nuances of the Creative Commons licenses and how they work with all the work that we're doing for open education at our different institutions. Just as a reminder, you will have the opportunity to ask some questions. Hopefully we'll have some time at the end. If you do have some questions, feel free to enter them into the chat window. I cannot guarantee that we'll get to them all, but hopefully we'll have some time at the end for that. And past that, we'll talk about a few resources that you have and ways that you can stay in contact with us. So without further ado, I would like to turn it over to our speakers for today to introduce themselves. Annalie, would you like to start? Sure. Hi, everyone. I'm Annalie Mon-Perry. I'm a Scholarly Communication Librarian at Arizona State University, which means that I specialize in Scholarly Publishing, Copyright, Fair Use, and All Things Open. Thank you. And Kelsey? Hi, I'm Kelsey Smith. I am the Open Educational Resource Librarian at West Hills College in Lemor. And since most people don't know where Lemor is, it's sort of in the middle of nowhere, California, about 30 miles south of Fresno, so Central Valley. My job, sort of my daily duties are working individually with faculty and in groups. I work exclusively with OER, and I don't really do a lot of that normal librarian type stuff. And most of my job involves searching for OER. Great, thank you. And Jenren? Hi, thanks for having me. I am the Assistant Director of Open Education at Creative Commons and love connecting with folks who have run through the CC certificate course. I know some of you are here and also working with a lot of you who are in the audience. So it's just an honor to be here. Well, thank you very much. I think that we have a lot of interest in this webinar today because hearing from a few experts about the nuances, the intricacies of Creative Commons licensing is a very popular topic. We found a lot of interest in it, so it seems like it's going to be pretty engaging. So first of all, like I said, feel free to introduce yourselves in the chat window. A lot of you have already done that. Last time I looked, we had almost 90 people logged in right now to the webinar, but feel free to introduce yourself. And like I said, if you have any questions, go ahead and put them into the chat window and we'll try to either incorporate that into the discussion as we go or have a little bit of time at the end if we have that opportunity. And just as a reminder, if some of you are perhaps new to the CCC OER webinars, the Community College Consortium for Open Educational Resources is a subgroup of the Global Open Education Consortium. And our goals are to expand awareness and access to high quality OER, support faculty choice and faculty development, and also provide some regional leadership for open education, all of course with the goal of improving student success. And we have been doing webinars for several years. Now they are archived on our website. You can go check out webinars going all the way back to, if I remember correctly, I think 2012. So there's quite a bit of interesting discussions and great resources that you can find on the website there. We also have memberships from all across the United States. We have several new members here and maybe some representatives from those institutions might be on the webinar today. So congratulations to the new members and thank you for joining with us. And if you are interested in more details about CCC OER or about how to become a member, you can always check out our website to find out that information. So without further ado, I will go ahead and turn this over to General Wetzler, who will provide us with a little bit of an overview on the basics of Creative Commons, the difference between all rights reserved and some rights reserved for those of us in the audience who may need a refresher on those basics or may be very new to Creative Commons license. So, General, take it away. Thanks so much. Well, first I want to recognize that there are plenty of people in the audience who also have far more expertise than I do. So if anybody has questions about what I'm saying or any revisions to what I'm saying, please feel free to type that in the chat space. So simply put, we're in an information abundant era as Cable Green also in the audience likes to say. What that means is we have this really incredible moment in time where we our landscape for information sharing has changed with internet and what that means is online connectivity has broken down the traditional barriers of geography, of our time and access that's traditionally kind of stunted our communication flows. But copyright or laws that grant exclusive rights to creators over their creations was built for the printing press and the information system that existed in that era of the printing press. So copyright was a way to incentivize creators despite the intensive labor, the expensive and limited print copies that were available in that era. And I'm actually I'm going to pause right there and copy and paste pretty good definition of copyright with a caveat in the chat space in case anybody wants a little bit more information. All right, I'm not sure if that's okay. So we now have this opportunity to share works far more easily and at near zero cost online and scale the number of people reached with these works on far more easily. But our current copyright system has not adapted beyond that kind of lack of adaptation. We're seeing other restrictions to this copyright system. So we see increased prices for published textbooks, the rise of subscription prices for publications. And not only are there increased costs, but there's also an increased amount of time that works remain under copyright. So in the 1990s, the US, for example, extended the copyright protections over works from life of the author plus 50 years to life of the author plus 70 years, which means it's almost at times, it takes almost two lifetimes to access the really wonderful resources that could be at our fingertips for reuse for innovation and for learning. So I think this presents us with a pretty significant challenge and many, many people recognize that there is that these restrictions despite the ease of information sharing in this current era is untenable. So there are a number of different ways to address this challenge. And I would argue that some ways are a little bit more effective than others. And I want to pause there and just recognize the difference between one set of ways versus another that might seem more similar on the surface. A lot of times people will hear about free resources and they'll also hear about open resources, open educational resources, open access to information and so on. But these are actually two very different terms that have very different connotations. So free, for example, might sound really good. But oftentimes in this this kind of ecosystem, it masks the transfer of costs from incurred within this kind of traditional copyright system incurred by one party that are kind of transferred to another party. So an example of that would be when students get access to free textbooks or academic journals. This is not actually free because universities actually pay for subscription bundles to these academic journals and they ultimately get their funds for for this from tuition from students. So this is an example of how free is not exactly what we're looking for when we're we're looking at open. Open, on the other hand, refers to unfettered access or access to resources in formats that allow for free download at the adaptation and improvements. It also refers to no time restrictions in accessing those resources either. So open is completely different from free, but it's often kind of mistaken for for free at times or freeze mistaken for open. So how do we actually open access to resources? This is where the CC licenses come into play. And I will recognize that there are other licenses that are open licenses, but Creative Commons has the the global standard for these open licenses that are also interoperable. So with open licenses right off the bat, creators can execute their own rights and grant permissions to other others that want to access their works. They can provide unfettered access or retain certain rights as needed. So CC licenses are kind of considered some rights reserved as opposed to the all rights reserved of copyright licenses. So I think on my screen, I'm seeing Matt Bloom's contact information, but I expect everyone else is able to see the CC licenses here. I'll just spend a moment to give a quick overview about the CC licenses, and if anybody is curious about learning more, I'm happy to follow up after this discussion. Okay, so there are six different licenses that Creative Commons affords. These are all contingent on four basic license elements. And so you can see that there are kind of four icons here that are kind of mixed together. So our first element is the CC by element or attribution element that is common on all of the licenses. It basically means that you can use an open licensed work as long as you give attribution to the licensor, generally the creator, but sometimes it's different. So that attribution element is kind of at the core of all of these licenses. You also have a share alike or SA icon, and it's the little arrow circle. This references our ability to work with open licensed works and use them, but the requirement to share works with the same or comparable license. So you cannot, for example, add on additional restrictions to a work with a share alike license as you remix it. We also have our non-commercial element, which means you cannot use an open licensed work with this non-commercial icon for commercial purposes. And I think we'll probably get into that a little bit more in the discussion later on. And then finally, we have the no derivatives element. And this means that you are entitled to use the work that is openly licensed with the no derivatives clause, but you cannot share any adaptations of that publicly. So while you may be able to make an adaptation of, say, a play or an image for your own personal use, you cannot share that personal use because it changes the license. So these are the four elements that undergird the six different license options or the six ways to provide permissions for downstream users of open licensed works. So when creators want to determine what they want their works to, how they want their works to be used, they can select one of these six licenses. And you can actually learn more about which license may be most suitable for you with our CC license chooser. And I'll get a link for that and post in the chat space. Here it is. So if we can go to the next slide, here you'll see the kind of the sliding scale of open. So this is a demonstration of how our licenses can roam from very open unfettered access to much more limited access to eventually get to all rights reserved access or all rights reserved copyright at the bottom. I will make a note that at the top you see our public domain tools or icons for public domain dedications. These are technically not licenses because the public domain is the kind of the vast pool of human knowledge that exists outside of the restrictions of copyright. So these are considered tools. The rest of the six licenses that you see are they work in copyright. So they work with those restrictions. So here you see those licenses going from CC buy to the very most restrictive license at the bottom to copyright. I'm realizing in terms of timing I'll stop there but happy to answer any questions about the licenses and how to use them during the discussion and following the discussion. Well that's great. Thank you. Yeah thank you very much, Jenren. I think that that is extremely helpful because like I said we have maybe some people on the call who are experts at Creative Commons licensing, far surpassing whatever you know small amount of expertise I might have and then there are also maybe some folks who really need the kind of reminder about some of the basics and that actually brings me to one of my first questions which I'll get to in a moment. I just want to say that one of the kind of basic requirements of all of the six Creative Commons licenses or actually the basic requirement is the need for attribution and I'm sure that a lot of us over the course of the next you know 45 minutes or 40 minutes will probably have questions about how to attribute and what's one of the best practices for that and we decided to actually just provide you with a set of resources for you know best practices for attribution how to build the attribution itself some tools for that and so there's a slide later on that is dedicated to some of these resources for attribution although feel free to share specific questions in the chat window. I did want to start the panel discussion you know after kind of having this this primer focused on all you know the basics copyright and the need for open licensing and how the open licenses work along with existing copyright law I felt like it would be a good idea to start with a very basic question or I guess suite of questions focused on this really I think interesting word work so again I almost I just want to say I almost put a slide in this presentation with like a picture of like a bunch of lawyers in a room to say like you know just to kind of like preface the whole thing and saying like we are not lawyers right nobody on this at least to my knowledge nobody on participating in this webinar is a lawyer so you know it's all prefaced with that kind of caveat that that these are just kind of opinions about how how the licenses work but it would nonetheless I think be very valuable for us to draw from our experiences and for you specifically on on the panel to draw from your experiences working with OER working with faculty at your institutions or with other organizations um working with people who are uh or thinking about the experiences that you've had with the open licensing and just give us a general sense and and I'll just leave this open to whoever kind of wants to jump in and respond but what do we mean when we talk about work like what is a work and then I've also heard other terms collective work I've heard obviously derivative work is something we'll be talking about so wondering if someone on the panel would be interested in kind of providing an answer or a set of answers to these questions here sure I'll go ahead and jump in um since copyright's one of my uh one of my hobbies I suppose you're absolutely right I'm not a lawyer um so when we look at works for copyright we look at things that can be protected by copyright and these are often things that are original works of authorship that can be fixed in a tangible medium of expression and this can cover a lot of different types of things um a lot of the things that we normally think about in education such as textbooks learning objects videos slide decks um and other sorts of things but there are some things that under that might not be protected by copyright that we might still consider a work so certain types of facts and data may not be protected by copyright or procedures or processes now they might be protected by a different type of intellectual property but they're not protected by copyright um so you know works can be a whole bunch of different types of things and some can be larger works some can be smaller works sometimes it could just be like a brief essay sometimes it can be an entire documentary right so there's lots of different sizes here uh when we look at a collective work that is often a group of different types of works that have been pulled together and they may be authored by a bunch of different people or they may be group authored where there's a bunch of people working together to make kind of a unified whole as opposed to pulling together a bunch of different kinds of works that are authored by different people not necessarily intending for it to be put together and mixed up into one cohesive bit um a derivative work is something specifically mentioned in copyright law as something is one of the exclusive rights of a copyright holder and that is building from one you know starting with one type of work and building to another type so a really good example would be I have a a work that I created and I want to translate it to another language so that would be um that would be a derivative work or perhaps I am starting with a uh a lecture that I've presented and I have a bunch of maybe a slide deck but now I want to take that slide deck and make it into an interactive tutorial that would be a derivative work as well so I'll start there and see if any of the other panelists want to jump in I don't think I could have said it any better than that um I just wanted to add on another example of what a collective work is and one that I see very often at the community college level um our faculty often um compile a lot of OER on our LMS which is Canvas and so that would be considered a collective work so they may apply a CC license to their whole course but there are so many different OER are like compiled into that so they may have videos images texts all of that organized into an online course so that would be an example of a collective work and I don't have anything else to add to that I think the the one kind of side note um in terms of derivatives we also sometimes refer to them as remixes or adaptations so if you hear people talking about derivatives or remixes or adaptations they at least in in my context we use them interchangeably that's great thank you so much now there are there are a couple questions um a thread of of kind of questions going on in the chat window right now that I think would be definitely appropriate to expand on this a little bit um it was brought up the the point I think believe it was buddy muse said to copyright to be copyrighted the work must be in tangible form as well um and then the response from um Andrea Troncoso was what counts as tangible obviously can't simply mean physical otherwise everything would digital would be open so would someone on the panel like to maybe address this question what do we mean when we talk about a work being in a fixed form or in a tangible form can we just kind of flesh out that definition just a little bit sure so we usually mean when we say fixed in a tangible form of expression it means that it has to be saved and in a format that can then be accessed later so um if I take a picture with my phone and it's on my phone that counts as a tangible form of expression if I save a document on my computer or uh in the cloud somewhere that also counts so it's really that has to be able to be preserved in some way that when you you can come back to it a later date you can share it with other people and it will still exist in that kind of state that it was in when you saved it right excellent I think that's very helpful um it also occurs to me that that we may end up addressing at some point in this discussion a definition of what it was like what does it mean to publish or share something too uh because it could just very well be that you so and maybe I'll just ask the panel this right now if I were to create a document on my computer uh you know and without the intention of ever sharing it I would still own the copyright and automatically for that work correct yeah you ask yep yeah so one of the questions that we had following up to this when we talk about a collective work or a derivative work I want to thank um Nate Angel actually for his um I some of us on on participating here in this webinar may be familiar with this analogy um that he's shared uh about remixing comparing the different ways that you can either uh curate creative commons license works in a way like this this would be like the tray would be kind of the collective work and then you know the individual compartments on the tray are the various you know I guess ingredients in the meal but they have their different licenses and you can clearly distinguish one from the other because you've got very solid boundaries there um the comparison between this kind of curation of openly licensed materials with uh what he calls the the cc smoothie um wherein you know it's the remix process you know putting it in the blender basically right you're creating this lumpy paste and it's it's virtually impossible at that point to tell where the strawberry ends and the banana begins and so thus in this smoothie the ingredient with the most restrictive license tends to overpower the beverage um I think my question here um because I've found this to be a very helpful analogy uh but my question is is the distinction always that clear uh are there situations where the blender maybe has only been you know pulsed like once or twice and there's big chunks of you know apple or something in there or is or is any blending at all going to require the application of the more restrictive license and another way to ask that question would be just in general you know where do we draw the line between curation of works and the creation of a remix or a derivative work I'm happy to jump in with the first stab so this is actually a really challenging question that comes up a lot in our certificate course on open licensing at creative commons and it's not always clear I think there there are a lot of cases where it's hard to determine originality and a lot of times what constitutes originality actually depends on a country's applicable law so I'm going to post a link to um a little bit more information on that if you're curious um I would say because the the lines can be blurry in our course we end up emphasizing um attribution first and foremost so if you if you do a good job attributing the content then it's almost it's almost secondary whether the content is collection content or remix content I'm not saying it's not important I'm just saying it can be very difficult to determine I was just gonna oh go ahead you go okay I'm just going to say that um there with there is no clear line and nobody really likes a chunky smoothie like we want it to be either a smoothie or a tv dinner and um and like you said you know no matter what you will have to attribute every one of those ingredients that you are using um so it's really it's so situation dependent it's it's pretty difficult to answer um so go ahead Emily I was just going to say that you know a lot of these questions are copyright you know everybody who loves copyright we always have the same answer to most questions is it depends so it's so hard to determine like how far is it like if I take a work um that is perhaps like a meme right I have a picture and I have some images on it and maybe I want to use or some words on it and I want to use it in a different presentation and me but I want to change the word how much of a change is that like how key are the the words to that image and that's a hard thing to determine like I might argue well it's not that big of a change maybe I'm changing one word maybe it doesn't really count as a full derivative or adaptation it's just a really minor change but I think that gendron's point to being really clear in your attribution and articulating what you did change that's kind of the key piece right yes I use this here's where it came from only my version had a different word here um so that nobody kind of mixes up what I'm doing versus what I took it from okay well thank you very much I mean so again it's maybe not always the most satisfying thing to to hear that the answer is it depends but it's just true and I and I appreciate that very much um I would like to shift a little bit to a question about fair use and how fair use this this concept of defending your I guess violation of somebody else's intellectual property rights is as fair use or fair dealing how does that work with creative commons licensing so for example if I were to um you know let me just ask like this is it possible to apply a creative commons license to a work in which you are fairly using all rights reserved content because I have I for one and I know some other people as well but I know that I found myself at one point under the impression that creative commons licenses in fair use defenses you know didn't didn't really work together like if you were going to put a creative commons license on something then you can't you shouldn't be like you know fairly using that work although I have been corrected in that in that misinterpretation of it so I'm just wondering what um you know is it possible to apply that kind of a creative commons license to a work where you're fairly using something and if so what considerations are necessary um I like the answer to this would be a yes but um so if you are using depending on fair use for a few things within a work maybe a collection you can apply a creative commons license to that but um you need to be very clear in what is being used fairly so um you need to be clear on if you are using something that's protected under all rights reserved copyright that you make that distinction that your creative commons license does not apply to that section um this I never encourage this among faculty that I work with if they are using diagrams or videos in their course that they want to share out openly that they then replace those with hyperlinks before sharing out um there is also a no to this question if you are using a like a whole pdf home um in your course that is all rights reserved copyright and you're relying on fair use you cannot apply a creative commons to something that you don't own the copyright to so a no and a yes but yeah I would agree with that and I really love to highlight so one thing to really consider is that um traditional publishers rely on fair use all the time for commercial uses right and we have big studios we have textbooks they use rely on fair use and so there's really nothing that prevents us from doing so with creative commons and open education content um I really appreciated um Kelsey's comment about making sure that you make it clear that this content this copyrighted content is excluded from our uh open license and then there's a really great um code of best practice in fair use for open courseware that came out in 2009 I'm going to post the link to that here in the chat um and this specifically addresses um these codes of fair use can really provide great guidance for common situations in open um situations and or so they provide good guidance for these different best practices and situations so you'll see that this is a common situation that happens in this environment and here's how fair use can apply um if your situation is very similar to this this fair use argument may work as well for you so even though you always have to do your own fair use uh justification for your use yes things can be different um there's always little individual changes but these codes of best practice can provide a lot of guidance thanks I'm looking forward to seeing that um resource myself um yeah I think the one thing that I wanted to reiterate is that creative commons licenses do not replace other copyright licenses and fair use is a limitation to copyright so um I don't I don't see fair use as um a good bad fellow or a bad fellow bad bad fellow um I think there are two separate things creative commons licenses and copyright can be restricted or can um have the limitation of fair use excellent I think that um those are great responses and again you know highlighting some of the complexity here but also I think helping to clarify maybe some misperceptions that that might be out there um I do have a question about other kinds of open content licenses um I guess just just to ask the question directly should we be remixing creative commons licensed content with work made available under other open licenses or even work that has been published with a non-standard permission statement as um you know we we probably have all seen at one time or another for example an educator maybe before creative commons existed you know somebody might have created a website you know a teaching blog or something and and put content on that and then down at the bottom just written in very much non-lawyer language um something along lines of well feel free to use this as long as you're doing it for x and y and not blah blah so I'm just wondering what um you know should we be remixing content like that um and if so what should we consider I'm happy to jump in with a quick thought on this um so I think when you are trying to look at the interoperability of copyright permissions you need a copyright lawyer so if you are going to try to remix or if you're trying to kind of merge these permissions um it's going to get very messy especially for lay people um like us um so I I would argue that it's it's not a good idea um I think a really good thing about the creative commons licenses is um they are interoperable you don't have to initiate a legal conversation with a lawyer every time you need to use or remix them I can definitely see some hesitation and I think that um that's absolutely right but I guess I would also argue two two quick things is that um for one thing if somebody's kind of giving some permission in advance even if it's not a standard license but if they say pretty clearly on their blog or something like that saying you're free to use this for this type of purpose um I think that it would be okay if you're using it for that kind of purpose but again you would want to make sure that you're clear in your attribution and your documentation that I'm using this by permission seen on this page um and I would also say something like and I guess the other thing is if you felt uncomfortable doing that here's where it's fine to ask for permission we can always do that and if and if you're looking at somebody's blog where they're giving that permission then you're in an enviable place where you think that you know who the copyright owner is at that point um and so you can always contact them and ask them for permission. Yeah I was also going to mention that and I've done that before where I wasn't completely clear on what the person was allowing and not allowing and uh I know that's not always possible maybe the person doesn't work for wherever or they have passed away but it doesn't hurt to reach out and especially if you plan on mixing it with a lot of other credit common license things you can tell them what your intent is and if it matches with what they planned for their work and yeah so it doesn't hurt to reach out. Well all right I mean so I think that that's great because at first it sounded like we shouldn't do it but then at the same time it really is a matter of the ability to contact the original author is a great potential recourse and I guess as a follow-up question if an author and again I don't know if there's even really like a solid answer to this possible but and I know you're not lawyers but if someone had published something in that way with a non-standard you know just expression of permission to use under certain circumstances and then you reached out to them is it appropriate and or possible to ask that person to release that content under an open license um and if so would it is is it not can is it possible that the license that they apply could conflict with what they had written the non-standard description before? Does that make sense? That makes sense to me I I think it would um like any other communication that you might have with a creator getting a sense of what they what they intend is um it's definitely possible and if they're willing to apply a sec license um great I think if it it likely as conversation unfolds it it could open up some conflicting permissions but again that that's probably more um an indication that just additional communications needed rather than an indication of um less communication needed I guess we're shying away from it. Well great so one of the questions that I whenever I do any kind of workshop about open educational resources or um in a lot of discussions that I have with faculty just as you know in the kind of support that I give in my job um one of the questions has to do with interpreting the non-commercial um licenses so the question that I have here I think there might be some confusion sometimes about when a use is considered commercial and I'm sure there's a lot of there are a lot of resources out there to kind of help us look at cases case studies and things like that but I'm wondering um you know how do we draw the line between commercial and non-commercial use when working with bookstores or print-on-demand services and and I guess and I know that this is kind of a nebula of questions here but um you know what about some of these other you know case examples what about the use of you know non-commercial content at uh for profit for profit institutions or charter schools or when soliciting donations um I was wondering if anyone could respond to these or maybe even provide a specific example where you have dealt with um you know kind of working out one of uh you know an issue related to the non-commercial clause um I wanted to mention that that the non-commercial license was actually purposely written sort of vague um because it's so situation dependent and that line there's no real solid line it's pretty blurry and um so from my understanding there's been some court cases and things about this um with as far as bookstores go they let's go let's do print shops first so a print shop you are allowed to um use a print-on-demand service um part of the Creative Commons license is giving you the ability to share and reproduce a work and you sometimes cannot do that without a print shop um so print shops are okay to use that's not considered commercial um what you cannot do is then um say your bookstore wants to print out an OER book that's non-commercial they can't mark that up highly they can mark up the price enough to do some cost recovery but nothing more than that and that's kind of been my understanding on the non-commercial in colleges um and then I will let someone else take away the second part of that question I'm happy to jump in um so I think the kind of a helpful distinction is um also noting that you're looking at use not the actual user so the if the intended use of the non-commercial um licensed work ends up being commercial then you're violating the license if the intended purpose or use is um is um a commercial venture for say a non-profit then you're violating the license if the intended purpose is non-commercial but it's being used at a um at like IBM or another um commercial venture then you are not violating the the license so it's really not about the the user it's about the intended use of the license I'm not sure if that made any sense but um I think you can you can have for-profit entities using non-commercial licensed um works without infringing on the rights well thank you very much for for everyone for um you know going and into some of this uh these philosophical discussions or I guess in some of the the nuances as I as I keep saying about them um this first set of questions that I had uh the questions we have addressed so far from what is it work to the questions about remixing and fair use and everything these were kind of all focused on drawing lines between definitions and and kind of looking at at some of those details but I was also there were there's there's a couple of quite one question in particular that um has emerged in discussions that I've had with folks you know whether at you know the open education conference or or you know various times what I've met with people who are trying to make these open education initiatives happen in higher education and a question that comes up has to do with intellectual property policies I mean different institutions will inevitably have different policies regarding the intellectual property rights of content developed by their faculty uh and staff and so to your knowledge how have some colleges dealt with issues or conflicts related to intellectual property policies and I think that's that's the part one of the question part two of the question is even more direct and it's you know should faculty and staff be releasing their work under Creative Commons licenses when no explicit OER policy language exists at their institution I think this is a great question I know that I've been working with our Office of General Counsel to try to clarify our intellectual property policies uh with respect to being able to apply Creative Commons licensed creative common licenses to our work so I think a key thing to consider is you should know what your what your intellectual property policy is and I think everybody who wants to create their own work and apply a Creative Commons license to it needs to understand what types of their works where they own the copyright and which ones are considered a work for hire where their employer owns the copyright and that's typically spelled out in either your employment contract or your institution's intellectual property policy and often it's kind of there's some gray lines where they may say something like well your syllabus and lecture and course content and learning objects that you submit to your students those are the work you do in the course and scope of your employment and that's your work for hire we own the copyright to that but if you do textbooks or you're doing scholarly publishing in your field we like to encourage you to do that and so you own the copyright to those works so and sometimes that's a gray line how do you draw a line between I'm making a textbook but I'm building it out of a bunch of modules that I created for my course so I think that you know the first you need to consider what it is and then secondly if you want to release a creative your work under Creative Commons license often there's procedures where you could work with your department head your dean your office of general counsel and say look I know that what you really care about is that you don't want me to take my stuff with me when I go so I'm not going to do that I just want to apply a Creative Commons license to it is there a process by which I can do that because I have to have the copyright in order to apply that license and so you have to clarify that process but I wouldn't just slap it on there without having that conversation and just from a perspective of a very small rural community college and others out there's out there where the intellectual property policies aren't really written and if OER is new on your campus there's probably no mention of open licensing in there so at West Hills we do have an administrative procedure that sort of mentions it but nobody was really educated on it at the time and it is going under revision and it did state that you know if you wanted to open the license something that had to be CC by and then you know faculty didn't really like that so but one thing I wanted to mention was it's a good idea to check if your state has some sort of policy our California Community College Chancellor's Office actually requires that anything funded by grants or contracts out of the Chancellor's Office must be released under a CC by license so that would you know proceed any of our language that we would have here on the college so if the work an instructor did was funded from the Chancellor's Office it would need to be released under CC by license kind of no matter what was going on here at campus so that's just something to be aware of you may have a state policy and just sort of be aware where the funding is coming from I have nothing to add on on the second half of that question I think you both covered it really well on the first part of your question Matt I you were asking a little bit more about some institutions who have dealt with conflicts related to IP or maybe some examples that we could share and I don't have many examples but I did want to share a recent case that I was aware of that was pretty interesting so basically there's been a legal case against Harvard for using images of slaves for a commercial profit and the lawsuit and I'll post the article right here the lawsuit says that the images are spoils of theft because the slaves and the images were unable to give consent so the descendants of the slaves in the images have may have rights to the IP so anyway a fascinating case I know there are a number of different colleges that have similar challenges but this this one really came to the top of my mind thank you that does sound very interesting I'm wondering if so we're getting pretty close to the end here and I want to leave about five minutes to go over that list of resources for attribution and some some just other brief little details about how you can stay connected with CCC OER before we do that the last question that I shared with with every with everyone on the panel or the last thing I was asking people to bring was maybe think about some kind of an experience that you've had a specific challenging or interesting experience that you've had working with open licenses so this is just very much open intended in in terms of you know your response but I'm wondering if anyone on the panel has any you know experience that sticks out to you from your time dealing with with the nuances of open licensing that you think you'd like to share happy to jump in so I think one of the challenging elements that I've seen in my previous job was not not particularly around the actions required for an open licensing requirement within federal grants it was at the time at the state department working on federal grant requirements for open licenses so it wasn't the actual implementation or actions needed but actually the the behavior change so I think with likely with any behavior change the biggest hurdle is often that kind of initial that initial mindset and addressing the underlying assumptions and mindset before change can happen so we we found a lot of different stakeholders that felt threatened by change and by what what loss it represented to them so I think one of the the quotes I I really like is that change represents loss to to someone so I think what we found was after months and months of work actually with cable and and other folks in the open community at the time we were getting nowhere with with kind of the resistance against our our work and kind of this this larger work that we were a part of creating a federal open licensing playbook just a playbook a set of guidelines for other federal stakeholders to understand how they could open license other cases of open licensing activity within the federal government and so on the resistance to that in particular was so so strong that the real takeaway for me was that that much of the focus has to be on that kind of like empathy and acknowledgement of loss for stakeholders within a system or at least perceived loss that that they have so I'm not sure if that is clear but the the challenge for me anyway it was more with the behavioral change and less with the actual open license that part was easy I saw a lot too there's a lot of resistance from people because it's a big change and a lot of the challenge I had was sort of educating our faculty on what these licenses really are and they're not a threat you know and they always wanted to choose the most restrictive one and what's been interesting is seeing somebody who really had no interest or was openly licensing their work but very restrictively which they're you know they are free to do and then a few years later seeing them dive real deep into OER and there we have people trying to develop VR psychology openly licensed stuff now and there's just that whole transition on campus yeah I guess I think some of the biggest challenges is finding the tension between you know a lot of people that want to support open education or open access and want to make their content available and they want to make use it and reuse it in different ways but there's a definite concern by a lot of academics that look I want to make this out there but I am concerned about commercial energies coming in and taking my work and then selling it and kind of ruining all this good contribution to the commons that I want to make and the non-commercial you know use does address that to some extent but then it can also be limited limiting in the kind of work and same with the share like so sometimes it's that tension like how do you pick the license that's going to best suit your intention for giving permission and access to your work well thank you very much all three of you on the panel for participating in this discussion and sharing your thoughts I think that so far based on that some of the other very interesting discussions going on in the chat somebody just asked whether or not the the full text of the chat will be shared and I will leave it up to Una to determine that but I think it would probably be fine and there so there were some other discussions going on in there and some great questions and resources shared so thank you everyone for participating this has been I think very interesting very enlightening in some ways as promised here is a list of some OER and licensing resources specifically there are a few in here when you look at the creative commons best practices for attribution you can use the open washington attribution builder which is like a really nice tool that you can use to kind of help you build the attributions if you need it there's all kinds of great resources on this page here that we encourage you to check out and try to utilize and finally of course or maybe not quite finally but remember that there's conferences coming up from the open education conference to the OEC global conference on and go to the website under get involved to see about those opportunities and if you are not on the CCC OER community email you do not have to be a member of CCC OER to join that email and I will tell you that while you get maybe 15 to 20 extra emails a day they are usually some of the most interesting emails you're likely to get some really interesting discussions in the open education community take place on that listserv so I do encourage you to join the community email to stay informed and to also you know find out about new resources that folks are sharing on a regular basis there and we also have our blog posts on the website as well if you want to check out some additional information about projects and experiences from folks in the open education community and last the shameless plug for the rest of our webinars in the in this fall series we've already done our two so far the first two but we've got one on october 16th about equity diversity and inclusion in OER following that November with a webinar focused on research studies looking at the impact that OER has on various metrics and then at the end of the semester we thought because of all the great opportunities for conferences this the this fall specifically the two that I mentioned we will be focusing that final webinar on kind of kind of doing a recap and providing some of our members a chance to reflect on their experiences and presentations at those conferences so we do encourage you to register early and join us for those and if you have any additional questions feel free to contact well according to this slide not me but feel free to contact me if you want Lisa Young and to Asu Tashjin are the co-presidents of CCC OER, Una Daley is the director and Liz is a support specialist and I thank you again for attending and have a great day


Women have been called upon to be their best in whatever they do and always stand out wherever they may be on the surface of the earth. These words of admonition were given by the wife of legal state governor, Dr. Ibichoke Sao Luwai speaking with newsmen at Government House Marina shortly after a pre-conference walk to a herald for the forthcoming 2022 National Women's Conference Bill to take place in Lagos. Correspondent Love Ikukuo Yedokun covered the work and now reports. This is a pre-conference walk. It aims to sensitize the people about this year's addition of the National Women's Conference, an annual event organized by Committee of Wives of Lagos State Officers, Kaosu. Led by the wife of the legal state governor who also doubles as the chairperson of Kaosu, Dr. Ibichoke Sao Luwai, the women worked from CMS Bookshop Lagos Island through Agile Street down to Campbell Stadium at Campbell Street through St. Nicholas Hospital then to Brook Street and terminated at the Government House Marina. Fielding questions from journalists, Dr. Sao Luwai highlighted more on the essence of the work. As you all know, Kaosu is the Committee of Wives of Lagos State Officers and he's celebrating his 22nd edition. We've been doing this for 22 years and it had been revived by the Distinguished Senator, Senator Ulure Mitiniumbu in the year 1999 but founded by Mrs. Oba Fumilayo Mobolaji Johnson, the wife of the former and first military governor of Lagos State. So we're creating this awareness so that people can come to the conference to learn, unlearn and relearn. In line with the theme of the hybrid conference, Spring Fort Stand Out, the Kaosu chairperson charged women to be up and doing and always take the lead. Last year we had a wake in 2021 and this is the continuation of 2021's conference. This time it's called Spring Fort Stand Out.  You must stand out even in adversity.  So we'll be expecting you there. Dr. Ibidyukesa Wulu also stated that just like previous editions, the inspirational women of the year and the inspirational youth of the year would be honored. The pre-conference work saw the wife of the deputy governor, Uluremi Hamzat, cutting habbe the cake. Love Ikuku Uyiduku, plus TV news. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Number five, they have their act together, both emotionally and logistically in their life. In other words, they're not going through some emotional chaos. Their marriage wasn't this, they're not pining over a previous relationship. They're very clean in their past relationships. They're not hung up on drugs and alcohol. You have to pay attention to these things. People that party too much. I said to past relationships, they've healed from their past relationships. I would venture to say 80% of the single population in the dating marketplace is still hung up on an ex, both females and males. This is not singular to a gender. They're introspective.


If you know you need to navigate 54 degrees from a location, how do you do this with your compass? First, open the cover so you can see the compass face. Then, rotate the bezel so that the number 54 lines up with the arrow marked near the hinge. Place the lanyard around your neck and holding the compass in the palm of your hand, or in your fingertips, raise it to eye level while keeping the lanyard tight. Keep your elbows tucked against your side. This will aid in studying the compass and give the correct distance from the compass to the eye of the user. Tilt the sighting mirror so you can see the face of the compass in it. Turn your entire body, moving your feet, not just the compass, until the red end of the needle lies within the red etched lines on the face of the compass, and where the sides of the needle are parallel to the etched lines. While keeping the compass aligned and level, look through the sight and find an object in the distance. This object will serve as your destination point. With that object in view, take the compass down and start walking toward the object.


Two people have been found guilty in two separate drug trials that were held last week in Hubbard County. Erica Randall of the Hubbard County Attorney's Office tried the cases of the state, and the first one was versus Kyle Edward Trisver on October 24th and 25th. The jury found Trisver guilty of one count of third degree controlled substance crime for the sale of exactly 3.5 grams of methamphetamine. Trisver will be sentenced next month on November 13th. In the second trial, Randall tried the case of the state versus Odell Branson. The jury found Branson guilty of one count of third degree controlled substance crime sale for multiple sales of prescription controlled substances. Branson will be sentenced later this year on December 3rd. Both of the cases resulted from the investigation and arrest of over 20 narcotic sales cases earlier this spring. Now the cases were investigated by the Hubbard County Sheriff's Office, the Minnesota Bureau of Criminal Apprehension, and the Paul Bunyan Drug Task Force.


Today, we are making healthy rice crispy treats. So whether you have kids or want to feel like a kid again, it's going to be a simple, minimal, although a little expensive way to enjoy that treat. And if you go into Whole Foods, any type of health food store, you'll see healthier organic versions of these treats, but there's usually a couple of ingredients in there, at least one that you don't want to be putting in your body and they also don't taste that great. Plus you're still paying quite a bit of money for them. So let me show you guys the ingredients. This might actually be the simplest recipe we've ever done because you only need three ingredients, grass-fed butter, organic marshmallow and organic rice puffs. Now you could make your own marshmallow fluff yourself. It's more of like a complicated kind of laboratory process. So it can be a little bit difficult to do. Same with the rice puffs. Like I'm sure you could fry these up and figure out how to make them yourself, but this makes it a whole lot easier. I think the rice puffs were $3 or $4. This was kind of expensive marshmallow fluff, about $8. And the butter should be around $4. So all in, you're spending about $25 to make quite a bit of rice crispy treats. So first I'm just going to butter my ceramic dish. This doesn't have to be baked. We're just going to pop it in the fridge. So with six tablespoons of butter, we want 16 ounces of marshmallow fluff. And I don't know how they made this, but it's kind of separating. It has a very sweet vanilla smell. And if you're curious about the ingredients in this, it's tapioca syrup, sugar, water, egg whites and vanilla extract. So I'm sure you could just pop all of those ingredients into the blender and make this for a whole lot cheaper price. I think we did an organic s'more recipe too, a while ago, where you guys might have seen me use this. And while that's melting, we can measure out our seven cups of puffed rice. Now these are kind of big, so I guess you could throw them in the food processor for a little while if you wanted to. I'm kind of just swirling the butter in the pan to melt it evenly into the marshmallow. Now stuff like this is better for occasional treat or someone that has a hard time gaining weight. Yeah, it's very calorically dense, concentrated in the fat and sugar. There we go. So it's mixed together, all buttery, sticky and sweet, still a little bit warm, so it's still pliable. All right, so the main reason we buttered this ceramic pan is so hopefully after we put this in the fridge overnight, in the freezer a little while, even this comes right out so we can cut it nice and easily. Hi, YouTube people. So the Rice Krispies have sat in the fridge overnight and I have my official taste tester here this morning. Hi, YouTube people. So she's gonna have her second breakfast of Rice Krispies treat. Actually, fourth third, fourth third depends on the day. That is one thick, oh it's sticky, okay. So I mentioned earlier maybe blitz up the Rice Krisp a little bit or they might even have some rice cereal at your health food store that's a little tinier in size, but this is still good. It's delicious and it's a lot healthier than having those blue-wrapped Rice Krispies treats and that's a bit of an understatement. You're dodging all of the potential agrochemicals that could have been sprayed on, all of the components of it. You're removing any of the actual preservatives and chemicals they add while they're making it and this is also probably about the same price as if you were to buy this amount of actual Rice Krispies treats and I think it's even higher in fat. So instead of dousing your kid's stomach with sugar and carbohydrates, at least you're giving them some high quality, saturated fats from the grass-fed potter. So thank you guys for joining me today. Definitely let me know how you like this and if you have any other recipe ideas that you would like me to try over the next few weeks, I'm thinking of doing a hamburger bun sometime soon but I've been procrastinating because it's a little difficult to do. So if you guys could go to frank-to-found-it.com you can check out all of my businesses and support me through those. Outside of that, thanks again for joining me guys and I'll see you for tomorrow. No.


Ben, I want to ask you, how hard are you used to throw? Well, I pitched and hit in college, and then professionally, I was an outfielder. But I didn't get a hundred like the other Verlander. I the hardest I hit was 92. Are you sure it's your brother? Not positive. He always used to tell me the FedEx driver dropped me off, but, you know.


Hey, everyone. Today I'm going to be running through a workshop on tuning your instrument with Google Teachable Machine. And by the end of this, we're going to be using Google Teachable Machine to create a machine learning model that'll tell you what know your instrument is playing out. So if you're playing in A, but you should be playing in D, you will know that. And we'll also create a web app to deploy your machine learning model so anyone can use it. Let's see if I can share my screen. OK. So yeah, a little bit about me. I'm currently a senior at the North Carolina School of Science and Math, and I'm also a writer at AITA. And that's what I'm going to be creating this workshop for. I was born in Singapore, but I've lived in North Carolina for 12 years now. In regards to computer science and programming, I'm mostly into web dev, so creating websites for organizations and just for fun, and also machine learning. So I try to make this workshop be that the intersection of those will be creating a machine learning model, and then we're going to deploy that machine learning model at the website.  I really enjoy swimming. I like biking as well. And watching airplanes is cool. That is the wrong side. OK, awesome. So outline for today's workshop, we're first going to be talking a little bit about audio recognition. What is it? Then we're going to be actually building our tuning model. Then we're going to be talking a little bit about machine learning, and then we're going to be actually creating our app. So this right here is a demonstration of what the finished app will look like. On the right here, you'll see our website, which is also accessible by this URL, I believe. So I just copy and paste that link into the browser. And we'll share these slides with you, so you will be able to play around with this link as well. But on my phone right here, you might not be able to see this. Let me turn off my brightness. But right here, we have a D note on my phone. So theoretically, my machine learning model should be able to detect that this is a D. So let me start this, play this, and you'll see that it is playing a D. Just to prove that it works and I'm not messing with you, try B. There you go. So that's B. I can also try A. So let me find an A note after this advertisement. We can try A. Right. So this is going to be a machine learning model that is able to detect the notes that you're saying, and also background noise, which it's detecting right now, which is correct. So I'll turn back to these slides, present. Yeah, so this is our app. It'll tell you whichever note is playing, so you'll know whether or not the note you're playing is actually correct. That's one feature. Another feature is you'll notice that when I demonstrate that model, I did not have to enter a single line of code. So for the apps that you're creating, you don't want it to be inaccessible to people or your target audience. So in our case, our target audience would be individuals who are more into playing instruments rather than messing with the specific lines of code and trying to get your web app working. So with the click above button, they're able to detect this and it requires no code. And it's also really easy to expand and add features to our app. So with this information, with this A note that we're detecting, we can maybe say, you're doing a great job. This is correct. Or you might be able to say, you need to play a little bit lower. We have all the data needed to actually do that. So you can go ahead and add a lot of features to this app. You can maybe even make it into a social app. So you can really essentially do anything with it. And yeah, just as a reminder, the link to this app is right here in the bottom corner. So if you want to go ahead and play around with it. Cool. So let's go ahead and talk a little bit about what audio recognition is as a whole. This is a very big umbrella term, sort of like what machine learning is. So audio recognition in short is whenever a program receives audio as input and interprets that audio to produce some meaningful output, typically these audio recognition models or programs are built on machine learning models. So maybe with what I'm saying right now in this workshop, an audio recognition model might be able to transfer it into a transcript. So whatever I'm saying might be actually written down. And that can be done using machine learning. Another feature of audio recognition is creating personal assistance. So if you have an iPhone, that's what Siri would be using. Or if you use Alexa, whenever you say Alexa, it'll recognize that you're saying her name and it's going to come alive. Speaker identification is another big one. So something, it's speaker identification in short is along the lines of what facial recognition is but using your voice. So identifying people based on the way they talk. And also an interesting one that I found online when I was doing research about this topic was animal noise classification. So maybe you have a microphone in the middle of the forest but you don't want to spend all that money and resources to actually install a camera there. So what you can do is you can only install a microphone and collect audio data which takes up considerably less room than image or like visual data. And you can actually create a machine learning model to classify those animal noises. So you're able to know which animal it is. And you might be wondering out of everything that we just did, why do we even care about audio recognition, right? After all, everything on this slide could be done by another human. Speech or text, my friend can write down what I'm saying on my line. They can answer my questions like Siri or Alexa would. So you might be wondering why do we do this, right? And the short answer is that while someone might be able to do it once or twice, it's gonna be very costly and slow in the long run. So as with all computer tasks, audio recognition is really good at saving time and doing things over and over again. So if you read on the slide right here, computers are good at making the same decisions repeatedly while humans are good at making difficult decisions. And largely with audio recognition, if we are getting into actually that field of like a bit of difficult decisions such as like actually recognizing what I'm saying. So this is able to save us a considerably good a bit of time. So maybe instead of like in a meeting, you just click a button and you're able to transcribe all that text instead of having to have someone as a dedicated taker. And in general, it just makes life a lot easier. So in order to do audio recognition, we're gonna need to do what's called machine learning, right? So audio recognition, you can think of it as a subset of machine learning and as such audio recognition models such as what we're gonna be creating today, it's gonna follow every single step any machine learning model would go through. So the first thing we're gonna do when we're building our models, we're gonna actually collect data and you'll see the form of how we actually collect this data and how we feed this to train our model, which is gonna be our next step. So training the model is essentially creating the mind, I guess you can put it that way. So our model, you can essentially think of it as a brain. We're gonna be saying, we're gonna have this data and we're gonna feed it into this brain and we want some meaningful output. And the way we check if we get that meaningful output is by testing our model. And once we're happy with our model's results through testing, we're gonna go ahead and deploy that model and that's what we did with our website that I just showed you earlier. So first step in machine learning is going to be data collection. So with any machine learning model, you're gonna need data, right? So that's why machine learning is often called to be at the intersection of computer science and data science because you do need data and you do need programming ability to do machine learning. And for data collection in our case, we're gonna be using seven YouTube videos as tuning examples. And the notes that we're gonna be classifying is A, B, C, D, E and F. I know there are other notes out there, but just for our example today, for simplicity, we're gonna keep it to these six. Just for an example of what this would look like is if I just click on tuning note A, this is what it's gonna sound like. I don't know if you can hear it, but yeah, so it's just an example of what the tuning note A should sound like. And we're gonna be feeding this audio from each of our audio clips into our machine learning model in order to extract data from our model about these data clips. So after that, so after we have all these data, we're gonna actually feed these data clips of these audio recordings into our machine learning model. And we're gonna be doing this using something called Google Teachable Machine, which has a super friendly web interface where you don't need to actually write a single line of code to train our model, which is really, really neat. So you'll see right here, this is the Google Teachable Machine interface and we'll be going through this again when we actually build it ourselves, but you'll see right here that we have to capture a bit of background noise, so our model knows what to filter out when we're classifying these clips. We'll have a couple of clips for A, so the way we're gonna train that is I'm just gonna hold up my phone right here for lack of a better method and I'm gonna click the play button and we're gonna record using my computer's microphone. And then we also have B notes and you'll imagine that we also have C, D, E and F. And then after that, we're gonna go ahead and click this train button and then we're gonna actually train our model. So all the training is essentially done behind the scenes and you won't actually need to worry about it. But if you are interested on knowing what actually does happen behind the scenes, this is a bit of the, like in the weeds, if you will, of what's happening. So don't worry about if you don't understand fully what's happening on this slide, it's meant to be a gentle introduction to the difficult stuff, I guess, by machine learning. So essentially Google Teachable Machine which is what we're gonna be using is built on top of something called speech command recognizer which is a pre-trained audio recognition model that already recognizes simple words. So it already recognizes zero, one, two, three, four, five, six, seven, eight, nine all the way, such as like down, left, right, stop, yes or no. However, we're not actually gonna be needing to use like these simple words. Our target for this model is gonna be recognizing notes, right? So that's why we're gonna need to add our own classes to these, to this audio recognition system which is gonna be ABCDE app and the background noise. And as such, or, and we're gonna do this using Google Teachable Machine which is no code and you're gonna be able to actually get back a model that you can actually embed into another website or app. It's pretty cool. So once you've trained your model, logical next step would be to make sure that your model works as intended, right? And this is usually pretty easy with models such as a text classification model. There's an objective metric on whether or not the thing you're classifying is correct. However, a Google Teachable Machine doesn't necessarily provide you with an accuracy metric. And you can imagine it'd be a bit difficult to tell you whether or not something is correct just if you think about it, right? So let's say I put an A note for 20 seconds. Maybe for 10 seconds of it, I get A back. And for the next 10 seconds, I get D back. But then I wanna keep adding on, right? So maybe for the next 10 seconds, I'm playing a C note and then, but it detects as D. So notice that it gets, as this adds on, it gets a bit more difficult to calculate what you actually define as accuracy. So what we're gonna do to solve this is we have to actually figure out by testing it if what we're doing is correct. So when we play A, we have to make sure that our model is helping A. So it will be a bit subjective. And as this line says, you have to make sure that you're happy with the results that you're getting in order to move on. And then after that, we're going to be actually deploying our model and making our model available for others. So this is either for others like the public or other programs to use. And this can take the form of many options, right? So what we did in this tutorial is what we're gonna do is we're gonna be actually making a web app or a website for users to use. Additionally, we can actually turn this into an app so people can interact with it on their phones. Or we can turn it into an API endpoint so other programmers can make a HTTP request and interact with our code. And since we're gonna be deploying this as a web app, we're gonna be deploying our code on GitHub pages. And this is the URL that I pointed out earlier. Cool. So I'm glad you got through that. A lot, a ton of rambling right there. But our next couple, our next section is actually going to be building our... Hello, everyone. So I'm getting, I'm back again with the workshop recording. So right here, we're gonna be actually building the model that our tuning web app is going to be built on. So right here, I'm at Google.com and I'm gonna be actually running through with y'all how to actually build this step by step. So right here, we're gonna go ahead and add to Google Teachable Machine right here. Because Google, it should be the first thing that pops up, I'm gonna go ahead and click on it. I'm gonna zoom in a little bit so it's easier to see.  So Google Teachable Machine, if you did not catch from the first part, is going to be a almost low to no code solution that allows us to build machine learning models. However, that might sound a bit too basic, but don't worry. This is actually gonna follow the same machine learning principles that every machine learning model goes through. So we're gonna have to deal with data collection, training, validating and testing and actually deploying our model. So we're gonna go through all four steps. It's just going to be without writing as much code, which is actually really nice, so yeah. So right here, once you go ahead and click Get Started and Google Teachable Machine, you should have a couple of options on what you want to do. For me, we're gonna actually be using Audio Project, but I'll go through a little short run through of all these. Image Project is going to be training item based on your webcam. So if you look at this example right here, it might be whether or not it's just the dog or you and the dog, right? So right here, the machine learning model would be able to predict whether or not it's just you in the picture or you and your dog in the picture. Right here in the audio model, which is what we're gonna be using, is actually we're gonna be actually training our machine learning model to detect what note of interest we're playing. It was actually pretty neat. After that is also a pose project. So you can actually see which poses a person is in. So you can check their posture, you can check whether they're doing a squat correctly, which is also really cool if you wanna integrate working out fitness with computer science and machine learning as well. Awesome, so from right here, what we're gonna do is go ahead and click on Audio Project and we'll be taken to this screen right here. This is gonna look a bit complicated at first, but I promise it's not. For at first, all we're gonna do is we're gonna be worried about this column right here. We're gonna have to record a couple of samples for background noise, which is essentially data collection. Then we're gonna go into class two, which is going to be, we can actually rename this A.  So we're gonna have seven classes in total. And what these seven classes are gonna represent are the different outputs of possibilities that a machine learning model would be able to give us. Just for an example of what that's gonna mean. So maybe we're gonna be to that kind of background noise. So we're gonna have to record a couple of samples of data of what the background noise currently sounds like in the room. We're gonna do the same for A, B, C, D, and E now. So with that said, let's go ahead and record a couple of samples for background noise this sample. So I'm gonna go ahead and click the mic icon, and I'm gonna go ahead and be quiet. And it's going to actually record a background noise. Cool, so we have 20 seconds of audio recording recorded for background noise. You'll see that this says 20 minimum, which means we have the exact minimum, which is absolutely okay. So what we're gonna do now is we're gonna click extract a sample. Now we have 20 samples of background noise, which is gonna be good. So our machine learning model is actually gonna be able to filter out what noise is the background noise and what note we're actually detecting. So that's why we're doing this in the first place. All right, so now let's go ahead and you can either record 20 more seconds, but just for my sake, and for time's sake, I'm gonna go ahead and not record 20 more seconds.  So I'm gonna go ahead and click on, sorry. Let's go down first and I'm gonna click on this A note, click mic, and we're gonna go ahead and get ready to record the A note and what that will sound like. So one way to do this is I have pulled up on my phone. I just searched up A tuning note on YouTube and I'm gonna go ahead and click one of the results that come up, this one. And notice that if I turn volume up, you can hear an A note sound. So now what I'm gonna do is, oops. Now what I'm gonna do is I'm gonna go ahead and actually hold this up to my computer and I'm gonna record four seconds of it and we'll see how that goes. Cool, so now we have four seconds of A note. We're gonna go ahead and click extract sample. And I'm gonna do that again, so let me reset this. Now that we have four more seconds of it, we're gonna go ahead and click extract again. And now we have eight samples, audio samples, and we have eight minimums, so we're good for A note. And what we're doing right now is actually, we're just, and all we're doing right now is we're actually just telling a machine learning model what each of these notes should sound like in order for it to be able to actually detect it later on. Right, so you're just like a human, how human ones, a machine learning model isn't magic, you have to give it like practice problems or training data in order for it to understand how to classify future examples. So now we're gonna go ahead and do the same for B. I'm gonna go ahead and click on mic and I'm gonna find tuning note B on YouTube. So I have tuning note B right here on my phone. You can see that. And then I'm gonna go ahead and click on it, record, and once those four seconds are up, I'm gonna go ahead and click extract sample, like we did with A. We're gonna record again, and we're gonna extract the sample again. And we're gonna do the same for tuning note C, extract sample, again, and we're gonna extract the sample. Now we're gonna do D. Let's go down, head mic, D, extract sample, do it again, and then extract sample again. Now we're gonna do E, almost done, promise. Hit mic, extract sample, one more time, and extract sample. Last note we're gonna be doing is F. So let's click mic, extract sample, and extract sample. Awesome, so now we have all of our samples that are needed, and essentially this is how we just perform data collection if you recall on the PowerPoint that we did before. Right, so now we have all of our data that's been collected. All we need to do now is go ahead and click train model. So now Google Teach More Machine is going to take a second, prepare all this data, and it's gonna feed it through almost a brain, right? And just like how when you do practice problems, you learn how to do them, Google Teach More Machine is gonna train on this dataset and figure out how to classify this data. And on the right you'll see that our model is actually being live, and this is how we're now in the testing phase. Right, so if I just be quiet real quick, we're gonna see that this should detect all background noise. And you see that's pretty cool, right, it did that. Now if I pull up a note such as I currently have F pulled up, if I just play F, you'll notice that's F, right? Pretty neat. Now let's go ahead and do, how about B? B was a little weaker at first, but that's okay because it got it in the end. Now let's try C. We'll see that C. We'll see that C actually gets it pretty good as well. We're gonna try D now. And D is really good, it got it automatically at first. Let's try A. It got A in a second as well. Now let's try E, I believe, we haven't done E yet. Maybe we have, and I have to go E. And we tried F before, but awesome. So now we have validation that our model actually works, right, which is super amazing, which means that now we can go ahead and click export model. So now all we just did in the past 10 minutes, I'll do a quick recap before we export real quick, is we went ahead and collected data from YouTube and these data links, I just searched up Turingo A on YouTube, Turingo B, Turingo C, and I'll include the actual links to these videos in the workshop description as well. And then we went ahead and trained our model. If you wanna play around with it further, you can actually head into this advanced tab, then you can play around with this and you can actually explore what each of this does. But it's not really necessary, as you saw with the default settings, we got really good results in training our actual, in the output of our actual model. Right, so yeah. Now what we're gonna do is click export and then upload my model. So we're gonna have a link that we can use. It's gonna take a second to upload. Give it one moment. And now our model is uploaded. So what I can do is I can go ahead and click this copy link, paste it into Google, and you'll notice that our model is right here being previewed for us. So just to prove that it is our model, I can go ahead and play E again and you'll see how it detects for E, right? Awesome, so the next step in this workshop is actually going to be a pretty big part of machine learning, which is actually gonna be taking our model and being like, hey, how can we make this usable for the average user? And we're gonna be doing that using actually, by deploying our model on a website, as a website, using GitHub Pages. And we're gonna be making that website using HTML, CSS, and JavaScript. So if you wanna stick around for that, also I'll see you guys in the next section of this workshop. Thanks. Hello, everyone. Thank you for staying with us for this workshop of tuning your instrument with Google Teachable Machine. In this section of the workshop, we're going to be actually taking the model that we already have, which we have right here, if I can just share my screen. Second, so if we can just look at the model that we already have right now, and we're gonna be actually taking this and making it into a website. So anyone, regardless of coding experience, are able to access this model. Okay, you might be wondering that's pretty difficult, but don't worry, don't fret. It's not as difficult as it sounds. So I can just go through the slides real quick. Cool. So now we have our trained model, which is right here, and the model URL at the bottom of the screen was my models URL, if you wanna look at that. And now what we're gonna be doing is deploying the model and the website. And what we're gonna be using is to actually show that it's gonna be three languages, HTML, CSS, and JavaScript. HTML is, can you can essentially think of as the skeleton of the website, which is essentially where all the content goes, right? This HTML is easily viewable with inspect element, and all the text on a website is actually always gonna be in the HTML. And then if we move on to CSS, the CSS is essentially the mark makeup of the website. So if you think of HTML as a skeleton, the CSS is what's gonna be the makeup, or what makes the website look pretty. And this might include changing the background color, the text color, and font weight, like bold, highlight color, rounded corners, hover colors, et cetera. Anything you can think of to make a website look nice, you can probably do using CSS. JavaScript is going to be more of the brain of the website. So JavaScript is what makes websites interactive, right? So when you click a button, what is gonna happen? That's gonna be dictated by JavaScript. In our example specifically, JavaScript is going to be accessing our microphone and detecting audio. And essentially for our build outline, what we're first gonna do is create our website files. Then we're gonna export our model to the site. Then we're gonna add a bit more functionality so it doesn't look as plain, and then we're actually gonna deploy our website. And you might be wondering, this might be a bit hard, but it's okay. Everything we'll be doing is gonna be online, so you won't have to do any local installations or anything. So we won't have to worry about any of that. And the way we're gonna start off with this is let's all head to a site called replet. It's R-E-P-L-I-T dot C-O-M. And this is how we're actually gonna be coding our website. So first thing what we can do is let's go ahead and click on create right here. Let's search for HTML. So we're gonna click on the first one. For this one, for the name of our site, I'm gonna say tuning instruments ML, like that. Then I'm gonna go ahead and click create repl. It's gonna take a second. And then you'll see right here, I'll just walk you through the interface of replet real fast. On the left right here, you won't really have to worry about any of these buttons at all. Right here, you're gonna have three files in your folder. So right now we have index.HML, which is right here. And this is what I was talking about with the skeleton of the website. Right below that we have script.js.  And then right here, this is style.css, which we can think of as the makeup of the website. And right here, when I click run, you'll see that we have our output or the output of our code right here. And just to show you, I can actually open this on a new window. And this is a live website. If anyone who goes to this link will access this Hello World website. Well, actually, maybe you might not because by the time you're watching this workshop, this link will already be populated with my code. So first thing we're gonna want to do is let's go ahead and take this code that I'm about to copy and paste onto the screen. Go ahead and take this code. Let's first copy this code. So all the way from Hello World right here. Let's go ahead and delete that. And then we're gonna copy and paste this right there. I will make this code available to you, probably via a bit.ly link that we'll show on the screen right now or either in the video's description. So keep on looking after that. So paste that into index.html then head to script.js. Then what we're gonna do is copy and paste this code into script.js. So paste that there and I'm just going to clean this up a tiny bit like that. And now when I click run, I'm gonna click start and it should once use my microphone, allow. And you'll see that we do get everything that was on this page, but on our own website. So just to show you what that looks like, I can play a tuning note. I've done before. Oops, let me disconnect this. I don't know if you can see this, but this is note C. And you'll see that C was the one that was populated just now, right? C right here. Except this website as a whole, to be honest, it's not very pretty. And we can actually change that. But if you are just happy with what this website is, as it is, you'll see right here, when I open a new tab, anyone can actually use this website now. And it was that easy to all the code that I copy and paste it, what's given by Google Teachable Machine, by the way. So all I have to do is I wanna test again. You'll see that the note C is playing. I thought that was really funny. Anyways, let's go ahead and head back to our code right here. And then let's add the code that we need to make this website look nice. So our end goal is gonna be turned to this website that we have on the screen right now to look something like this. Oops, not like that. But something like this where when I click start, it's gonna show loading for a second. And then, and right, it'll play that. And it'll show like that. So what we can do is let's go ahead and go back to Teachable Machine. I'm gonna go ahead and add a little bit of CSS code. And I will make this available as a link as well because the goal of this workshop isn't to teach you like as much CSS as more of machine learning. But essentially right here, these are all gonna be classes that we can use in our HTML. So go ahead and replace all that, run. You'll see that immediately it looks a little bit better. We changed the, we have changed the background color and the width of this. It's not all the way on the edge. Next, what we can do is we'll head into index.html. And then what we're actually gonna do is let's go ahead and add a couple of classes. So first one, let's say, set of this div right here. Let's just go ahead and create an H1 called tune your instrument with Google Teachable Machine. And then let's also make it super obvious for the user what note it's currently playing. So we're gonna say H3, current note like that. Let's go ahead and say span. So I'll hit tab and then we're gonna say, and as default, but we will change that as needed. Next, what we're gonna say is create a button. We're gonna call this type is equal to button. Then we're gonna say, give it an ID of start-button. And we're gonna say start. Lastly, oh, we're gonna, so that means we can remove this. We can just put all this later at the bottom. And all we have is label container, which is what we need. Now when we run the site, we'll see that our site looks like this. Well, we can also delete this. That right there, actually. Now when we run it, we'll see that it looks like this. Which is a lot more similar to where did we have it? Let me pull up the original site real quick. Which is a lot more similar to what we have right now. That's our old one, that's our old one. So these basically look the exact same now. Next thing all we're gonna need to do is go ahead and now really nothing happens when we click start, but now we're gonna need to add that functionality. So now let's go ahead and head to one second, script.js. And let's go ahead and grab some stuff real quick. So let's go ahead and say const result container equals document get element by ID result. Then we're gonna say const start button is equal to document dot get element by ID start dash button. And you'll notice what we're referencing here is ID result is going to be this right here. So that's how we're gonna be changing this value. And start button is what's gonna be this button right here. So that's how we're accessing in the JavaScript. So this JavaScript is essentially talking to our HTML file, right? So next let's go ahead and create an event listener, which is essentially what's going to happen when something happens, right? So let's say start and dot add event listener. We're gonna say click and then we're gonna do this a little bit cryptic syntax and we're gonna say console.log button was clicked. Now when we run it and let's open this in a new tab like this, refresh it. Now let's see what happens when we click this button. Button was clicked, right? Every time I click this button you'll see this outputs on the console. Let me just refresh that. Ready? Click. It shows there. Click it again. It shows it was done two times, three times. Pretty neat, right? So now we have a way of interacting with this button in JavaScript. So now we can say is whenever we click it we want to start button dot inner HTML to say loading instead of just nothing happening. And then we're gonna say init. Init is going to be a function that we're calling and it starts right here, which essentially tells the model to start working. Next what we're gonna add is we're going to add a couple of classes. So what we're gonna say is cost classes is equal to, make sure you have a map to a. Then we're gonna say b, oops, b like that. C, C, D, oops. And keep doing this until background noise. And this is just what we're gonna do for our CSS classes, by the way. This is how we're gonna make it look nice. Anyways, now let's scroll down a bit is what yours should look like. And let's head to this init function. So once we go ahead and go right here we have this code right here. Let me minimize this, it's a bit easier to read. Make this larger. So we have something like this. Then we're gonna say, this looks all right. Gonna say that. Next thing we're gonna say is start button dot display dot style. Oh, sorry, that was backwards. Dot display is equal to not cool. Next, what we're gonna say is, why is this not liking this? We don't actually need that, actually. But now what we're gonna say is, and this all this does is it's gonna remove the button from the screen when we click it. So now scores is equal to result dot scores, max score. We're gonna have, let's go ahead and grab the maximum score. So we're gonna say const max score is equal to that dot max scores like that. Then we're gonna say const max index is equal to scores dot index of max score. So we're gonna go ahead and grab the class with the most likely probability, and it's index. Then what we can say is, and then result container dot class list dot remove, result container dot class list. We're gonna go ahead and clear that class list. Then what we can say is result container dot inner HTML is equal to class labels max index plus open parentheses like that plus max score dot two fix. So we're only gonna show two decimal places plus close parentheses like that. Oops, that should be a plus sign, not equal sign. Then what we're gonna say is result container dot class list dot add, we're gonna say classes class labels, let's say max index. So we're gonna go ahead and add, whichever note we're detecting the highest is a corresponding class right here. That's gonna have how we color coded. Then what we can say is for I let I arrange so that this code is all right, for I arrange child nodes dot scores equal to, so what we can actually say right here, we can say to fixed, and then let, oops, and then let's, after we grab this child node, we can say is current node, we have to declare this select current node is equal to label container child nodes I like that. And then we can say, now we can remove this because it's wordy. Then we can say is current node dot inner HTML is equal to class prediction. Then we can say dot style dot background is equal to transparent just to set this up. Then we can say, if scored at I is equal to max score, then we can say is current node dot style dot background equals light green, cool. Let's go ahead and test it. New tab, let's go ahead and test this note. Oops, I have an advertiser not playing. Another advertiser on YouTube. We have A like this. So it is actually detecting C, which is quite a bit weird. Let's try a different one. And let me actually try taking, yeah. So it's detecting background nodes correctly. Let's try C. C is being detected correctly. Let's try B. It's actually a bit confused. So it can't figure out if it's C or B. So in these situations, it might be good to actually, I'm gonna change the input source because when I did record this, I was using my computer's microphone and now I'm using my headphones. So it might be a bit weird. So I'm gonna go ahead and change this source. Cool. So I think it should be able to detect another input source now. Let's go ahead and click start. Yeah, so this is detecting B correctly. So it is just because of the input source training is different. So you see that B detects correctly now. I can try A again just to prove that it was not actually broken. Okay, is there? No, I'm gonna try D. There you go. You can feel free to play around with the rest of it, but I just want to say that the reason why it was not working just now is because I was using my headphones microphone, which is actually different than my computer's microphone when I trained the model on my computer's microphone. So it's important to note that however you train your model, you have to test it in the exact same way. Because if you're training it one way and then testing it another way, it's not gonna work out, as shown when I just did. So yeah, well that was the entire workshop of tuning your instrument with Google Teachable Machine. If you are interested in the actual code used behind this website, feel free to check out my GitHub, which is shown right here. And yeah, thank you so much for joining us today. And we hope to see you in the next one. Yeah, and I forgot to mention, but this website is actually something that you can share with anyone, right? So the reason why we created it on Replet is because they will actually, once you write your code and you click run, this website can be shown to anyone, right? So this URL, if I open it up in a new tab, I access it here, anyone can access it, right? This model still works as normal if I test it in a second. But yeah, there's absolutely no barrier with you sharing this model with your friends, your family to show them what you've created, right? And just prove that it works, right? So this is the A-note. And yeah, so once again, thank you for watching this workshop and we hope to see you in our future workshops.


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