Welcome everyone to our session with Blackboard and Hypothesis, Leveraging Hypothesis Social Annotation in the Age of AI. So today, we're going to be talking about different ways you can use Hypothesis Social Annotation either in conjunction with AI or maybe to discourage your students from using AI in your courses. So the agenda for today, I want to start out by just first explaining what Hypothesis Social Annotation is. So I recognize that not everybody who's joining us today might be familiar with Hypothesis. So I'll just do a quick five minute intro to show you what Hypothesis allows you to do in your Blackboard course. And then I will hand it over to our guest today, Robin Bell, who will talk about the need for AI literacy and how she is facing AI literacy with Hypothesis in her own courses. And then we'll wrap up with some ideas from my end on how you can start using Hypothesis to approach AI with your students. So today, it's going to be me and Robin speaking with you. My name is Christy Carolus. I'm a customer success manager here at Hypothesis and I'm an instructional designer. I've been supporting faculty with using educational technology for about a decade. And I also use Hypothesis in my own courses that I adjunct. Robin Bell is joining us from South Texas College where she teaches English. So she'll be giving us the rundown on how she's been approaching the use of AI and her courses with Hypothesis. So like I said, I first want to kind of set the foundation, set the stage here for what it looks like to annotate with Hypothesis for those of you who might not be familiar with the tool. So I'm going to hop over into my Blackboard course. So I'm in my Blackboard learn a original course and I have a document loaded into that course. And I'm going to open this document on my screen. And basically what Hypothesis is allowing me to do is open this document from my Blackboard course and it overlays the sidebar on the right hand side of my my document within the course. So you can see throughout the sidebar that there are some conversations happening and I want to highlight a couple of things that are going on. So if I'm looking over into the distance over here, it's because this is where I have my course on my monitor. So here, you might notice that we have different pieces of highlighted text on the screen. So if I hover over text in an annotation on the right hand side, you'll see that text changes color on the left hand side so I can see exactly what portion of text a student has annotated. So Enrique has asked a question about the course learning goals in my syllabus. Later on, I have annotated one of the course learning goals itself. And then again, there's an annotation about the online format and the schedule. So each annotation is anchored to a very specific piece of text, whether that is a word, a phrase, a sentence, you can choose exactly what you're going to comment on with Hypothesis. And we're calling it social annotation because the default setting here is that everyone can see everyone else's annotations. So when you see on some of these annotations, the option to show replies. When I click on that option to show replies, it expands almost like a threaded discussion. So here me and Enrique are having a conversation about a course learning goal. And then further on, Jamie and Enrique are having a separate conversation about a different tool that's being used in the course. So we can have almost these simultaneous conversations over a text using Hypothesis that I directly opened from my Blackboard course. So Hypothesis allows us to have these simultaneous conversations and have students actively read and comment on the text as they're reading the text. So if I wanted to add a comment here, I could simply select the text that I want to annotate, click the annotate button, and then I can add an annotation here. And post that and everyone in the course can see what I have annotated. So that's kind of the basic rundown of what Hypothesis social annotation lets us do in Blackboard. A little bit later on in the session, I will talk about some of the details of how that integration works with Blackboard. But first I want to talk about why you might even want to be considering using Hypothesis social annotation in your course with these new generative AI tools that have been popping up. So why is there even a need for AI literacy and how do we approach it with our students? And this is where I'm going to turn things over to Robin to tell us a little bit about how she's approaching AI literacy and how she's using Hypothesis social annotation in her courses. So Robin, do you want to talk a little bit about your slide here? Yes, I will. And I actually think it's really important to talk about this because this slide here represents the very first step that I take with students before we even really talk about or do anything meaningful with regard to AI. And so I did start last semester kind of in an experimental sort of phase where I think that AI was more like the elephant in the room. It was something that we all knew about. Many of us were maybe starting to experiment with it and consider how we were going to navigate that, whether we were going to allow it, whether we were going to try to use it as a tool. And I think after I used it for a small amount of time, I already saw the vision and the possibility. And I know that just a moment ago Christy brought up the fact that the things that are going to be addressed here are how and why we might even want to be even talking about AI literacy or how we might want to use it in our courses. But I know that some people may have a completely different philosophy than I do and that's certainly valid too. But I think the way that I see it, I feel like it's very important for students to, for me to build buy-in for students and to make an appeal to them that's related to academic integrity, but not, you know, honesty for honesty's sake in the abstract, but more in a practical kind of way where students can see what's in it from their vantage point in terms of learning how to use AI ethically, responsibly, and effectively to improve their workflows and foster real learning and boost their creativity. And I start out by telling them, and last semester when we started this, I didn't have this slide here, but I do have this slide now. And it's one of the first things that I show students to kind of make this appeal. I tell them that if AI can outright do your job, or if it can outright do your homework, what useful you be to your future employer. And my philosophy is that I need to get them to buy into the value and the integrity of their degree. And if they're doing things to circumvent real learning, then they're devaluing the worth of their degree. They may very well have that piece of paper at the end of four years, but it won't mean much if they can't do what the degree claims they can do. And kind of I think the prevailing internet wisdom is that AI won't take your job, someone who knows how to use it will. And this has kind of informed my philosophy and my decision to try to use and incorporate AI as a tool, not as a focus, not as something to ever do our work for us, but something that we can work into our workflows to improve them and evaluate them and learn how to use it better so that we can be more productive. And one of the things that I like to emphasize with students is this notion of us being the pilot and AI being the co-pilot. And that's kind of a riff off the language that Microsoft uses and GitHub and, you know, the links on this slide actually go out to those places and I do talk to students about that. And neither of those platforms are intended to replace the humans that use them, but they were designed, you know, with the mindset of making our workflows more productive. But it's important that we are the human in the loop and that we are and that we remember that it's humans that are behind AI. It's humans that are in front of AI using it. And I think we have a real opportunity as educators to have something to say about, you know, how and what happens not only in our classrooms, but to give students things to think about in terms of how will AI impact their future career, their workflows, their own education, and maybe even more importantly the education of their children. And I will say before I jump in a moment to share with you the some of the assignments that I have created is that when I beta tested this last semester, I did it very slowly and very deliberately and I created transparent workflows and a culture of kind of trust and collaboration. And I must say that it was really inspiring and that the students validated my, for the most part, they validated my positive assumptions about them. And they were absolutely vital in helping me kind of transform what we were able to do with three specific assignments last semester and the vision and the execution of what I have in mind for this one. So I would like to maybe jump from this to Christy, can we, is it okay for me to share my screen. Sure, so I'm going to stop screen sharing so Robin can show you all a little bit more about the assignment she's used with AI and hypothesis. Okay. Okay, there we go. Okay, so this is a little Google site that I created to kind of house some of these things that I think it'll be more convenient for me to show them to you this way. So what I have here is an overview of an AI hypothesis assignment that I hope students find to be wildly fascinating and to serve as something that opens up bigger conversations that have more relevance to their majors their degree plans their future careers and so on. So the name of the assignment is called art in the age of AI annotating the intersection of creativity, technology, and it's broader implications. So in a nutshell and in a moment I'll show you what it actually looks like, but it's a very easy assignment it's high interest, and I chose the subject of art, because I think art is for one thing it's uniquely human I think it's something that we associate, primarily or maybe exclusively with humans, the notion of being create creative. And if you look up the definition of art in the dictionary, it will speak to the fact that it is, you know, it is something created by humans. And I do think with the advent of the AI generated art platforms like mid journey and Dolly, and, and others that, you know, we have a new tool that can be used by artists and people who may don't maybe don't have an art background and I think art is something that resonates with just about everybody. What we have over here is I have, you know, kind of the PDF document here that I could make full screen but I think I'm going to leave it this size for now. But I always start when I have an assignment for students, I give them an assignment overview. And so this gives a summary and an upshot of the assignment which I'm just going to tell you about here. So this involves two steps, two layers, if you will. And the first step is that students are going to annotate an actual chat GPT thread containing a conversation between me and chat GPT for on the subject of art. AI art, all the, and all the things that that might encompass. So it's a pretty broad conversation that goes in a lot of different directions. And this becomes the, the first text in this little mini AI unit that I have that is currently sitting outside of the other units. So this is something that we kind of have on the back burner. And this will serve this is my introduction to the class about AI and why we're using it and establishing a rationale. And I hope that this assignment helps me do that. So that moving forward, I can my lessons regarding AI will be course specific to to my course. So in the first step, they're going to actually annotate that thread. And they're going to annotate it up to a certain point. And let me just scroll down for a moment. I'm going to show you the actual GPT thread. That I have embedded here. So one of the things that I love about Blackboard Ultra is the ability to embed content. And so you can create pages that are very much like the one that you see here. They're almost like a mini webpage, if you will, you can embed content, you can embed all kinds of things. And I love the fact that I figured out last way last semester, a way for us to annotate this thread and now it's even easier, because all of the platforms being AI they all have a way to annotate the text that you can generate there natively from, you know, clicking at share link right inside. So this is the actual chat GPT thread. And part one, they're going to annotate this according to the directions that I have for this and I'll bounce back to that in just a moment. It's a pretty lengthy little conversation. I hope it asks some interesting questions. But the more we iterated the less formula it became, it became, and the more it became a more organic kind of conversation. And the idea is that students are going to annotate this up to a point. And then there's a part, when I get to part two, down here, let me see I'm looking for a part two. And it should be coming here we go part two. So I actually use part part two or I use chat GPT to help me create almost like a custom made hypothesis text. So up to this point, it's the conversation. And then at this point, students are going to take a pause. And I'm going to give them two weeks to play on some AI art generators that I'm going to show them how to use. So by this point, I will have already showed them some images in mid journey, which I use quite a bit. I use it in my courses for almost all of the images that I create and almost all of the images on this Google site are also from mid journey. But I'm going to show them how to use AI art from some free platforms and give them a couple of weeks to play on that before we really dig in and talk about the ethical concerns, the issues, the controversies, the use cases, and kind of get into all of those kinds of things. So they're going to annotate this pause. And then I've created some anchor points and I actually gave chat GPT this instruction, and I said, Hey, I want students to have some anchor points below because this is going to be in a hypothesis assignment. And they're going to anchor some of their AI art here and then they're going to discuss that experience and they're going to look at some of the questions that I have that follow and wrap up this chat GPT thread before we move on to the next. Part. And so then at the end, I have some kind of culminating questions that I want students to look at and explore. They don't have to answer all of them. But they do have to answer some and then obviously they're going to have to go in and reply and engage with their with their classmates. And I do typically the reason why I have eight anchor points up here for the AI art is because for all of the hypothesis assignments that I create. I tend to put students in groups of eight. I have, you know, relatively small classes. 22 to 24. So the groups never exceed eight. And so I wanted eight anchor spots so that students can do this. So my thought here was is that I can give students some hands on experience with a real AI tool and do it in a way that they might find to be fun or interesting in some way. And then we're going to talk about the ethics and the implications of that on a broader scale. So let me go down here to the bottom so I have some more handouts here. One of which is a guide to exploring AI art so I do need to show them how to how to do that so when they do that pause if they're in a face to face class I will have gone over this in class if they're in an online class I have instruction in the course plus I have a handout showing them how to get started with the being AI image creator. For example, this is free and you get 100 free credits a month for fast image generation. And then thereafter if you run out of that you can still generate images but it may just slow down a little bit. So it's totally free and it's pretty good. And I think it's fun for the students to play on. Our school also provides us with an LMS version of Padlet, but this could also be accomplished with a free version of Padlet. They have an I can't draw feature which is powered by AI. And so you can create free art there as much as you want for no outlay at all. And based on the image here I'm pretty sure this runs on an older version of Dolly. So all they have to do here is create a Padlet, click on the I can't draw and they put some descriptors of what it is they want to try to draw or paint or create an image of, and it will even create, you know, kind of photographic images of people as well. And then I have an example here. You can see that here I said, you know, to draw do Hogwarts painted in the style of Henry Rousseau.  And so it's just kind of look a little handout showing them how to do it, and telling them that, you know, this is supposed to be fun don't stress about it nobody's going to be, you know, upset if you don't create a masterpiece. And in fact, I actually want them to kind of see that I want them to see that sometimes you can create something that is quite beautiful. Sometimes you can create something that's weird. Things that, you know, are there are anomalies in the images. And I want them to see the good and the bad in terms of the output in the generative AI art images. Now, once they get done with, with that, the second part of this there's another hypothesis assignment that's really the second part of this. So the first part again is the chat GPT thread up to the part where they pause play on the art generators, then they come back and kind of culminate that start thinking about the bigger implications of that. And then what we have here is a two page article on art and the science of generative AI. And what I really like about this are two things. Number one, it's short. And number two, it contains a claim that kind of inspired this whole assignment. It makes the claim that if we, if we can talk about the impact and the implication and the use cases of AI in one domain, such as art, because art is something that most people can identify with and can relate to in some way. If we can do it in that one domain, it can be a good gateway for you to open those conversations in other disciplines or other areas or domains of life. So this is part two, and part two does require some lateral reading. So it requires them to do outside research and they would have to include that in part of their, their annotations. If I bounce back up here to the instructions, you know, in the beginning I pointed out that, you know, there's an overview here, and it also explains the two layers so they can kind of see in an upshot. What it is that we're doing, they see the simple little rubric that I have in terms of, you know, how this is going to be graded parts one and parts two of the chat GPT thread. And then part two, the annotation of the little article and there's a little summary there. I think or at least I hope that that is useful. For each part of the assignment I have very specific instructions and sample posts. So in this one annotating a chat GPT thread on AI art, it's very step by step with screenshots showing them, you know, where to post that they need to post to their group. And then I have, these are my, these are my tag types. I do require students to tag all of their annotations because I have very specific objectives for each thing that I have them read. They may not change from reading to reading but if it's something super specific or I have some kind of alternative objective and that will be reflected in the tag types. And for some things I give them a range, you know, I might say 8 to 10 annotations, sometimes I give them a hard number, and I'll tell them that you need at least so many of each kind and so many replies. And then I do try to provide examples for every single, you know, thing that they could come up with. In terms of the different tag types. And let me see if I can, and here at the bottom so here's what I want them to do on those anchor points. This is where I would have them share their artwork on those anchor posts and then talk about those culminating questions. And I've also included instruction for them on the other handout that you may remember from just a moment ago on how to play with the AI art generators. One of the things that I'm going to encourage them to do but it won't be required is to create an AI art gallery on Padlet. So no matter where they happen to create that AI art, whether it's, you know, in the free AI art generators that I recommend to them, or whether they are choosing to try, you know, other ones, they can house those on that Padlet. And I've encouraged them to, you know, post a picture like the one that you see here to the anchor, but then include a link to your AI art gallery. And so my hope is that that will encourage them to do that to create more kind of have some fun doing that and share the results with with their classmates.  So, if I scroll down just a bit more, this will show you what it looks like in hypothesis. So this is actually what it looks like inside one of my Blackboard offer classes, but I did drag these to the top of the screen so that we could just see that this is what the links look like and this is what students would see. I do put a description underneath kind of telling them, hey, this is part one, this is part two, and I will often put, you know, if it's a two part assignment, I will generally put, you know, the due dates there just so they can kind of see them out in the open. And just like Christie showed us a moment ago, this is what it looks like in Blackboard Ultra, you can see the actual chat GPT thread there and this is me just as a model for students annotating, you know, out to the side and also trying to model and show them that they can include multimedia when it's relevant to do so. They can include images when it's relevant to do so. They can include articles. So I even have a link to an article here, which isn't, it's kind of an encouragement on the chat GPT thread, but it is a requirement on the article. And then one of the reasons why I really like this assignment is I do think that it is adaptable to just about any discipline. I do know that English teachers can get away with a lot in terms of borrowing from other disciplines in terms of things to write about or read about or talk about. So I do think that even other disciplines, even if you didn't use this assignment as is, it's a good gateway. It's a good attention getter. It's a good conversation starter. And even if you use the little two page article, it's a good gateway into opening the door to explore and to discuss AI in meaningful ways as it applies to your discipline and as it applies to the disciplines that the students are choosing to study. So I'm really excited about this. I have just opened this in my classes. This is something that I have not, it's brand new. So this is still in, this is my first iteration of it. So I'm really excited to see what students are going to come up with. And this is my kind of more structured introduction to what then what I did last semester and I have deliberate lessons that are also presented in class and in the online environment.  So I do have, you know, kind of just a little blurb here about how valuable now this I did do quite a bit last semester to the extent that I could in the last third of the class, and that's teaching AI literacy as it applies to my course. I created a lot of chat GPT threads that we looked at and analyzed and evaluated in class showing the limitations of AI showing the capabilities of it and showing how it really is a skill in terms of learning how to use it and use it effectively. And I also had and required students to use AI in very specific workflows as part of some of our assignments, and they had to turn in their chat GPT threads to me in the assignment link. They had to post them to a padlet that I created for that purpose. And we did reflective writing on anything that we did with regard to AI. So we weren't just using it to use it. We were also reflecting on it. And in some cases we compared the outputs that we got there to other human inputs. And it was a really a good experience overall. And, you know, that was kind of my my first draft iteration of it. So what I'm doing this second semester is kind of a second draft iteration. It's bigger. It's I think it's better. It's more deliberate from the beginning. And it's probably going to take me another semester or two to get it, you know, to really get it right. But I'm kind of excited about, you know, the possibilities here. So that's kind of in a nutshell of what I have to share, you know, with you today. Christy, I guess I'll go ahead and turn that over. Turn it back to you. So, thanks so much, Robin. And taking that deep dive with us on how you're using hypothesis and AI in your course. I think it's super interesting the journey that you're taking with your students and the level in which you're having them. Not only use AI, but kind of critically look at what AI is generating and what it means for them and the future in general. So I did put for anyone that is interested the link to the Google site that Robin was showing us into the chat. So if you want to access her assignment instructions, you can see them in the Google site.  So maybe you're thinking, wow, Robin has done a whole lot in her class with these assignments. So she's really did like, you know, gone into the deep end and really bought in with making sure she's approaching AI with her students. I have some ideas for if you want to try and like dip your toes into the water a little bit first, how you might do so with hypothesis in Blackboard. First, I want to go through a couple of key details about how hypothesis works in blackboard for schools that are hypothesis subscribers. So if you are a hypothesis subscriber, you do have hypothesis available in your Blackboard course already, and I'll show you where you can find it in Blackboard. It basically allows you to create any document you have loaded into your Blackboard course annotatable. The students and yourself do not have to create logins or leaves a Blackboard course site. You're annotating the documents within Blackboard itself. So your Blackboard course documents are becoming annotatable with hypothesis without leaving the site or having that extra barrier of creating an account. You also can grade annotations in Blackboard. So I know Robin did kind of briefly show the rubric that she's using with her annotations and her assignments. If I hop over back to my annotation assignment I was demonstrating before you may have noticed the top there's this grading bar at the top of my document. If you're an instructor and of course and you've enabled grading on your assignment only you'll see this and I can select a student to grade and it will filter only that students annotations within Blackboard. And I can grade that student and that grade will be sent to the Blackboard grade center. So it's really easy to give students credit for their annotations and look at just one student annotations at a time. Robin also mentioned this feature as she was discussing the way she sets up her assignments. So in the beginning I noted that the default setting with hypothesis in Blackboard is that everyone can see each other's annotations in the class. So everyone's annotating together. But hypothesis does integrate with group sets in Blackboard. So if you do want students to annotate in smaller groups that you have a really large class. And it just doesn't make sense for 100 students to be annotating together, or maybe you're in a situation like Robin then you just want to have eight students annotating together. You can set up your group sets in Blackboard and hypothesis will automatically set up a separate annotation space for each of those groups. So there are lots of flexible ways that you can set up your hypothesis enabled readings in Blackboard. So now that we know a couple of the details of how it's integrated into Blackboard, I want to talk about some of the ways we can approach using hypothesis with AI in mind in our courses. Hypothesis makes our Blackboard readings more active, visible and social. It makes their readings more active because it's asking students to engage in metacognition as they read. So I think it was really powerful that Robin mentioned, you know, like if you're not putting the work into learning, you're not going to get anything out of it, right? You putting hypothesis annotations on an assignment is asking a student to reflect on that reading to think about what do I not understand about this? How is this connected to what I might have learned before? How is this connected to what we've done in this course or in my other courses or my own life experiences? So having students annotate is asking them to actively engage in that reading instead of just being kind of a passive participant in the course. It makes reading visible for us as instructors because we can see where students are having those connections, where they have questions and how exactly they're connecting to the reading. And it makes reading social because, like I said, the default is everyone's annotating together or everyone is annotating in smaller groups. So students can learn from one another. And this is in my own experiences. Whether my students are annotating about AI or not, they tell me that they like using hypothesis because seeing their classmates interpretations of the text help them better understand and comprehend the concepts that are being displayed in the text. So how do I think social annotation can help in the wake of these generative AI tools? And this is for everybody. So whether or not you're using AI or having your students use or examine AI in your course, I think just using hypothesis along with your readings in your blackboard course can help. And it's for the three reasons I have on this slide. Consistently using hypothesis social annotation helps emphasize the process of learning over the final product. So why we have to think about why are students going to be turning to generative AI tools in the first place? They might be turning to a chat GPT or Bard or one of the tools out there because they're feeling pressure. They have a big paper coming up.  And they're feeling the pressure of that grade and the need to do well on that assignment. Incorporating hypothesis social annotation throughout the semester as formative assessment emphasizes this process of learning before we get to that final product. Because we have to be honest. Learning is not always about being right. We can't always be right about things we don't know yet, right? We might fail and we can practice that art of failing when using hypothesis. Students can learn to explore and critically think about the text and do it in a way where it's okay if they're not 100% correct. So we bring in that value of the learning process by consistently using a social annotation assignments. And that can help take some of that pressure away from those large summative assessments. It also encourages continued engagement with the course materials throughout the semester. So another reason students might turn to generative AI tools when they're reaching their final assignments is because they didn't do the reading. As the students progress through the semester, life, the demands of life come at them, right? They have jobs. They have four or five other courses that they have to think of. They have family obligations and their exams and their papers and all these things that they have to be held account for are taking precedent. And oftentimes they stop doing the reading, maybe unintentionally just because they run out of time. But asking them to engage with the readings with hypothesis ensures that they're going to keep engaging with the readings through the semester. We actually have a case study on our website with UT Austin showing that using hypothesis does increase student engagement with the course readings throughout the semester. That means that by the time they're getting to those summative assessments, they might not need to turn to generative AI because they've been keeping up with the reading. And that's, again, more anecdotal feedback from my students as they like being held accountable for doing the reading. They like the way that it kind of forces them to check in and keep up with that material. And finally, using hypothesis social annotation cultivates the student voice. So having students annotate in kind of a more informal way throughout the semester on your course readings help you as the instructor better understand what that student's writing style sounds like. So you can kind of get a sense of what that student naturally sounds like when they're writing, when they're turning in summative assessments, you can like think about is this what they kind of sounded like throughout the semester. So I think consistently using social annotation throughout your readings through the semester just is a good foundational practice to encourage students to that not only value learning but keep up with the course materials throughout the term. So I know Robin went into like a deep dive of how she is using AI, lots of AI tools, chat, Upt, mid journey, Dolly, in her course, but whether what are some other ideas for using hypothesis and blackboard with generative AI in mind. So, if you don't want to use AI at all, I know there are some people who might be like AI is not for me. Then in that case, you might want to just use annotation as a formative assessment. So, like I kind of was just explaining, you can use hypothesis through with each of your readings throughout the semester and give students maybe, you know, low stakes formative assessment for that low stakes points for completing those assignments. So maybe they'll get some participation grades for completing their annotations throughout the semester. And that can also provide scaffolding for those larger assignments. You can pinpoint where students are struggling before they get to do those exams into those essays based on how they're annotating. You should also consider incorporating community annotations into your summative assessments. So, can the student annotations actually become course material that could be referenced or cited. Part of my favorite reason for using hypothesis is that it de-centers me as the authority with my students and my students become active scholars and active contributors to the course. So, I actually like to bring those contributions into summative assessments and have students cite each other's work, cite each other's annotations when they're creating projects or writing papers. Because that is something that Generative AI does not have access to, right? ChatGPT doesn't know what students are annotating in your courses. So if you can bring those things in, that can be a way to make sure that students are incorporating your original course materials into their work. And then similarly to what Robin has walked us through, you can have students critique AI generated text using social annotation. So your students themselves don't necessarily need to use ChatGPT or BARD or something to do an activity like this. You could simply put an essay prompt into ChatGPT and see what it comes out with. And have your students critique that essay through a number of different ways. And we have a number of starter assignments available on the slide, and you can take these instructions and use them in your own courses. So you can have students work as editors, for example, and critique ChatGPT's writing style and writing form. Did ChatGPT make a good argument? Did it provide good evidence for its argument? Was it missing things stylistically? What was its voice like? You could even have students compare ChatGPT written text to human written text using social annotation and look at what are the strengths and limitations of each. I think it's also important to have students fact check ChatGPT or other generative AI. So if it has created a text that you can have students annotate, have students really rip that text apart that ChatGPT or BARD or whatever has created and fact check every single fact that is in there. So we know generative AI sometimes tends to hallucinate. I don't know if students always realize the extent to which it can hallucinate. So having students fact check find citations for each of the facts in an article can be revealing for them on how they can or cannot depend on AI. You can also have students act as content experts. So I saw this a little bit with Robin's example with artwork. She had one of the art examples she gave. It was like having AI create art in the style of a specific artist. I forget which artist it was exactly, but you could have students do this with writing. So have a ChatGPT write a poem in a style of a specific poet and then have the students critique. Did a ChatGPT do this well? What is wrong about the style here? What is correct about the style here? So there are a lot of different ways that you can have students use annotation to critique generative AI's work. In addition, we have just general annotation starter assignments here. So these starter assignments are specific to AI. If you're not interested in using AI in your course and you just want to have students annotate documents, we have samples of instructions for students annotating documents here as well. Because I do think it is important to prompt your students, give them guidance for annotation. If they don't have that guidance, sometimes they're not sure how to contribute meaningfully to that conversation. So I want to use the last few minutes to show you how easy it is to get a hypothesis reading set up in Blackboard. At this point in time, hypothesis readings work best with open educational resources. So you can use PDFs with hypothesis, open textbooks and open educational resources like press books, open stacks, library texts are very common. You can use public facing web pages and online articles with hypothesis. We recently introduced the ability to annotate YouTube video transcripts as you watch a YouTube video with hypothesis. And you can also now at certain schools use hypothesis with JSTOR articles and vital source e-texts. Another thing that Robin kind of hinted at is that you're not limited to just annotating with text as well. There are lots of different things within an annotation that can make reading a more multimodal experience. So you can add images to annotations. So that's where students in Robin's assignment are annotating the text and then adding their own mid-journey images to their annotations. You can embed videos into annotations. You can use latex to add equations to annotations as well as adding external links and tags as well. So again, you can really make the reading a more multimodal experience by adding these different things which can enhance understanding of concepts by not just understanding them through text alone. So if I have a hypothesis available at my institution, in Blackboard Learn, I will find it in my content area. And it would be under my build content space. So you will find it and by scrolling down and finding hypothesis toward the end of the list of the build content space. And I'll click on hypothesis here. I can enter the name of my hypothesis assignment. And I always want to apply instructions for my students. So I am going to just kind of copy and paste some of our sample instructions here into my assignment. If I want to use a PDF, I'm actually going to ignore this attachments area here. This is not why I want to attach my document. I first want to set up my instructions and then I will add my document as a second step. If I want to enable grading, I can do so here and add a due date and submit. And I've created kind of a shell for my assignment at this point. Now if I scroll down, I can find the shell that I've created and click into that to see the different options I have for adding a resource to a hypothesis enabled reading. So I have, like I said, the option to add a URL using from a website, a YouTube video or a JSTOR article. I can also grab a PDF from Google Drive, OneDrive or Blackboard. In this example, I'm just going to grab a URL and throw that in here. And my hypothesis assignment is basically ready to go. So in Blackboard Learn Original, that is the setup process. If you have hypothesis available at your institution, if you're using Blackboard Learn Ultra, the process is not any more complex than that. It's just a different click path that you would be using. And we do have some instructions in the slide deck as well. So as we come to the end here, I want to review what comes with being a hypothesis partner. You do get pedagogical support from our customer success team. So the customer success team provides training for your school, one-on-one consultations for your faculty. You also have access to some of our resources, like our Hypothesis Educator Forum and our support team. Partnering with Hypothesis also gives you access to Hypothesis Academy. And Robin did take Hypothesis Academy over the summer and became a Hypothesis Certified Educator. There are two week courses designed to help you create your own annotation assignment. In a way that best supports your students' learning. So we have two topics that run throughout each semester, Social Annotation 101 and Social Annotation in the Age of AI, which works well for our current topic today. And you also will have access to our weekly partner workshops. So those of you who are not currently Hypothesis subscribers, if you are interested in learning more about gaining access to Hypothesis in Blackboard at your institution, we have a spring starter promotion going on right now. And you can reach out to the email on that slide to learn more about how that could work for your school and have someone reach out to you. And customers, if you need any assistance, please reach out to the customer success team so we can help you get started with Hypothesis. But we are rounding out in the last few minutes today and I want to thank everyone again for joining and see if anyone has any final questions for the day. I see Lee has your hand raised. Did you have a question, Lee? You should be able to talk if you have a question now. Anyone else you can also either raise your hand or put a question into the Q&A box. I'll share the link to the slide deck in the chat one more time to make sure everyone has access to that. Great. Well, it looks like everyone might be okay for the day. So thanks again for joining. Thank you to Robin for sharing her assignment and her approach for using AI with Hypothesis in her Blackboard courses. I really appreciate the time that you've all taken to attend at this busy time of year. I hope you all have a great start to the term and please reach out if there's anything we can do to help. Thanks so much, everyone.


The narcissist feels helpless when you do this. When you move on from the narcissist. When you leave them. When you cut contact with them. When you move on with your life. They feel as though they no longer have the ability to control you. They feel as though they no longer have the power to influence or direct your behaviour or the course of events. They only had the ability to do this because they tricked you into giving your power to them. They have no power of their own. They have to manipulate you into handing your power over to them. So that they can then possess the ability to control you. To maintain influence and authority over you. When you leave and cut contact with them. When you move on with your life. They no longer have the ability to control you. Because you are no longer given your power to them. You cannot move on without holding your own power. And using it to make that decision to move on from them. Which then leaves them powerless. It leaves them feeling helpless. Without ability, influence or power. It also leaves them unable to control themselves. But this isn't because you have taken something from them. It is because you have stopped giving them something that rightfully belongs to you. And that is your ability, influence and power. Instead of handing it over to them. Where they can then use it to control you. You have made the decision to keep it to yourself. Which then leaves the narcissist feeling helpless. Without a constant source of supply to depend on. The narcissist has no ability, influence or power of their own. They are out of control within their minds. They have difficulty controlling themselves. But what keeps their behaviour in check. Is when they can use you as their emotional regulation tool. They use you to regulate their emotions. To maintain their false sense of worth, self esteem and self importance. Which is why they always had such a desperate need to control you. Your freedom posed a great threat to how they saw themselves. The narcissist is imprisoned in their own minds. But for them to get a taste of what it really feels like to be free. They have to imprison or enslave their victims. It creates the illusion that they are free. When they observe the boundaries that they have created for you. When they lock you into their world. It creates the illusion of freedom for them. But they are not free. They are actually being controlled by their own minds. Their own emotions. And the only way that they can regain any control. Or even the illusion of control over their own minds and their emotions. Is to control you. They have to project their flaws and insecurities onto you. They have to try to make you feel the way that they do. Only then can they feel better about themselves. Only then can they feel like they are free. But even then. It isn't real. It's just an illusion of freedom. When you leave the narcissist. When you cut contact with them. That is when they begin to realize that they never had any ability, influence or power. They begin to realize that it was yours all along. They begin to realize that they are not free and they never were. They just created the illusion that they were free by imprisoning or enslaving you. And when you finally do leave them. That is when they become imprisoned. They become enslaved by a prison of their own making. Thank you for watching. I hope this video resonated with you. Please like, comment, share and subscribe. Click the bell icon to receive notifications for my future videos. If you would like to donate, my PayPal link is in the video description. Coaching inquiries you can email me at naks.coaching at duma.com. Thank you for watching and I'll talk to you soon.


Thanks everyone for coming. So we had a little extra time, so I thought we could do a fun little demo of this. So I just made this up, so bear with me. So I will need three volunteers from the crowd who are willing to crawl in front of the audience. Do I have any takers? OK, that's one. Two more. Two. And I need a third. Someone to go on their knees. Third, great. So if the three gentlemen could just stand over there. OK, I'll explain why they have to crawl, unfortunately, because this was not planned, so crawling is my workaround. So this is like a project I did with my six-year-old at home. And it's do-it-yourself snow globe. And in the talk, we'll go into detail of how it's built and everything. But I brought it with me. This is the actual globe I made with her. I brought it all the way from Bahrain, which is near Dubai. So flew across Europe to get here. And so now there's a little kind of wire here, a conductive thread. And if you make physical contact with it, the alarm will go off. But the catch is it has to be with your skin. And I didn't want to walk around barefoot. So you guys will crawl. And once your hands contact this, the alarm should go off. So whoever's going to go first, come by and let's see if this alarm system works. Just crawling your hands and knees. And hopefully, when your hand touches this, the alarm will go off. So let's see if it works. So he's intruder detected. Very nice. Thank you. So who's next? Let's see if you can go undetected. Very sneaky little intruder here. Oh, he's going low. Oh, we got him. We got him. My third volunteer is sneaky this one. Oh, he saw the thread, didn't he? I've seen people do that. I've seen people do that. See, that's why I do this in the dark. We usually dim the lights, and then you can't see the thread. I don't know if you guys can see the thread, but it's like really thin. But maybe we should turn off the lights. I'm just kidding. Then you guys won't see anything. We can pass the mic around if you guys have any questions about this for a few minutes before I start the actual slide itself. I don't know if you're going to cover this in your talk, but I just wonder how it's powered. Some kind of a battery rechargeable battery? Yes. So a lot of times when you have these portable things, power can be a bit of a headache. But the computer that's used in this, what's great is it just needs three AAA batteries. It's a regular battery pack. And I put it at the bottom, because the first model you'll see was on the outside. And my daughter said, this is ugly. You have to fix it. So I had to go and learn a little woodwork and build this whole case so I could conceal the battery pack at the bottom. But it's basically running on AAA batteries at the bottom that we think. Open up with some handy-dandy velcro. And that's the battery space. So that makes it really easy. Like, there's no concern about recharging.  Cool. Any other questions? Sorry, so the trigger is the? So there is this super-thin, conductive thread. This is just like thread that you'd have in your clothing. But it's made of stainless steel. And they have some funky manufacturing technique where it feels as thin as regular thread. So you could use any wire. But I intentionally wanted to use this because it's so thin that you could kind of conceal it and carpet. And if you dim the lighting a little bit, people can't be so sneaky like that third little volunteer and sneak by it. So that's what they were touching.  It won't conduct. But it has to do with your hands or your feet. Bare feet. So how do you actually detect contact? What's the what's why you're actually measuring? Yeah. So basically it's a capacitive touch. And it has a little capacitive touch sensor on eight sensors on this computer. So I connected to one of them. And you can attach any wire, anything that conducts electricity. And it just detects changes in the resistance. Any other questions? We have five minutes of questions that you must ask. Or I can go straight to the top. This side? It was a very good question. And it's funny you mentioned it because kids like you are very sneaky. So when I started, I had a very long thread. It was like five meters. And I was like, cool, they put it everywhere. Then there's no escape.  It's not going to detect something 10 meters away. So generally, you can use 1.5 meters, which turned out to be quite good because a lot of the doorways in our house. So we just pick the spot where you can't walk around. That's how I solve that problem. Doesn't work everywhere. If you had a floor that was a conductive, would it still work? So that would be even better because this conductive thread, it's not super resistant. But if you look here, for example, these are metal, these pieces. And if it's something like metal, you can really get a great distance. For example, this metal trimming here. I bet that could go a much greater distance. So it depends on the material. But with the conductive thread, because it's thread and everything, it's not a super good conductor. They also have this thing called copper tape, which conducts great.  But the problem with that stuff when we're doing it is it's very visible. So I had to match something that you could conceal and would conduct the electricity, which this is the compromise kind of thing. Sorry? That's a good question. I haven't tested it on mice. Could you repeat the question? Yeah. So from what I'd seen in a lot of the videos. So this is a project I made from scratch with my kid. But the actual conductive thread is part of the computer. And they have tons of demos where they plug it into a potato. And then you can play music with a potato or animals. All sorts of stuff. There's tons of stuff that can conduct. It doesn't have to be a perfect conductor that you could go and use. So you could make a fruit bowl, I guess, system that if someone touches an apple, it goes, wee, wee, wee, and says, back off my apple. But yes, there's a lot of options in terms of what you could use for the capacitive touch. We can start to talk. Great. Yeah? So then we will have more time for Q&A. We'll have 25 minutes of talk to figure it out all your amazing questions. So let's begin formally. Please give it up for Marwan. Thank you. So I'll go through the talk. And then we'll have another Q&A. I'm sure when you see the presentation, you'll have a lot of questions because a lot of the technology I go through in detail. So this is a project I did with my six-year-old. This is a six-year-old in question. And basically, you can buy these little do-it-yourself snow globes. You fill it with water, choose any dolly, put glitter. And then there's a little computer in the base. And when we built it, and you'll see the code, it operates on two modes. One's called the rainbow mode, where it just goes and does circles of colors. And the other is the intruder alert system I just demoed. The rainbow mode is just for decoration. So you can just put it and marvel at the rainbow colors that come off of it. And the intruder alert is more for setting up an intruder challenge. So I'll show both of the code and the rest of it. So now the structure of the talk will be how I assembled it, how you put it together. The code, all the code that's involved will go through it. And then the third bit is I use this as a platform to teach computing to my daughter. And she's six, so I had to keep that in mind when I was teaching her this. But it turned out well, because the hardware was actually quite designed for teaching and for kids and everything. And then what I did, I taught her aspects about sound and light, the physics of sound, frequency, and how visible light is red, green, and blue, and how that can be represented by using this as a platform for that. So let's see the assembly. This is the computer that was used. It's a running circuit Python, which is a fork of MicroPython. So this is a trimmed down version of Python that runs on bare metal on this kind of limited hardware or low end hardware, cost $25. And what I love about it is it has everything we need built it in. So I'd seen other projects that require soldering, and I didn't want to do anything that I couldn't do with my daughter. And a soldering iron, you have to be wary. So everything that was done here, I did with her. So she really felt like she was part of it, and definitely her personality went into it from choosing the doll to the way the wood was decorated and colored and the rest of it, which I think made it more fun for her. Now, the way you work with this device is it just has a USB connector, a connected computer. It'll appear as a USB drive. There's one text file called main up py. You just edit it and text editor of your choice, and then you can get the code set up on like that. So it was very quick and easy to get started and get immediate results, which made a big difference. This is one of many projects I attempted with her, and she has a certain amount of patience. She will tolerate a certain amount of wait time to get a Raspberry Pi connected and booted. So this was a great success with her. So I'll just compare it real quick to another popular platform, the Raspberry Pi. And the three things that really make this just great for me is it has all this IO built into it. Everything that was used, I didn't have to add. Like it has the lights that does the green and red alarm and the rainbow and the speakers and everything like that for the alarm. Compared to Raspberry Pi, where I would have had to get a breadboard to wiring and the rest of it, and she would have been more frustrated waiting. The other thing I'd say about it is when you turn it on, it turns on immediately. It runs your code. So Raspberry Pi, they take 30 seconds. And 30 seconds doesn't sound bad, but for a six-year-old, it's like, what's this thing doing? That's 30 seconds of my life. I'm never going to get back. I turned it on. It's not doing anything, right? Well, that's the moment you turn on the power and immediately goes into play mode for the kids. The battery is also a lot easier than Raspberry Pi because they just support these triple-8 batteries that you can plug in and get going. So these are the components that were used to build it. Snow globe kit, that's a hollowed out base that this computer fits into. And that's the computer, the triple-8 battery and the conductor there. So these are the retail prices on Adafruit's website who sell all of this stuff. Some of this you can find on Amazon UK and X-Ware, like Circuit Playground Express. But the Snow Globe kit, I could only find it on their original website. So this just gives you a sense of the pricing. That conductive thread, I only used 1.5 meters, but I don't know how long that role is. So this is my attempt at doing an exploded view of the base. So I was like, how do I levitate? So I got these crocodile clips, and then that's kind of just trying to show you how this gets assembled. And essentially, you add water, glycerin, glider, and dolly into the bottom bit. The glycerin just makes it thicker so that when you shake it as a snow globe, the glitter hangs out like snow more. So that's the dolly, rammed in upside down. And you're going to have to glue her bottom so she doesn't. Do not glue the dolly's bottom, as I did not. This is what happens. So you don't want to be there. Because what do kids want to do with the snow globe? They want to shake it, right? You want the snow to move around. And then it's like, Baba, what's happening with the doll? Why is she upside down? That doesn't look cool anymore. So I had to go remove the whole thing and do it again. And one thing I'd suggest is I used a glue gun to stick it to the bottom. And it worked really well. And the nice thing about using a glue gun is I'm scared to super glue with my daughter. If I glue her hand to her face, her mom will be very annoyed with me. Well, with a glue gun, generally, it's safer to do it around the bit. And I'd seen some of the guides online and don't follow them to see pain. They use regular glue. And then the mom or dad is just waiting for two hours for the glue to dry. So with this, it dries in 30 seconds. Glue guns usually see. It's a quick process. Whoops, I skipped the slide back. So this is the first attempt. And I'd seen an online guide, so they just stick the battery to the bottom of the base. I did that. And then after I put it, it kept falling over. So it stuck a piece of glue of wood. So it wouldn't fall over. And I marveled at this. And my daughter said, this looks like garbage. So I had to go back to the drawing board. It's very demanding, six-year-old. And I just wished to please. So then what did I do? I built this wooden base with her. Again, I got her involved. This is just this pulse of soft wood that I used and cut to do that. So it's two pieces, these two long pieces. And we just measured it out and made the base that way. And then I cut holes through the plastic.  But then I made this more elaborate hole that could fit the whole battery holder through, which helped me when I was bringing it to London on an airplane. And I didn't want them to be like, hey, that's very exciting. What is this? So I separated the computer and the battery and packed them separately, which you could do when you have it like this. And then a side aspect of using a wooden base is she had a blast decorating and personalizing it. And she's written her name on it and dated it and kind of personalized it. So OK, so the battery is quite straightforward. It's just a AAA battery, as you put it. I glued a piece of Velcro to both sides because I don't like to permanently glue stuff that I want to remove. And that allowed me to remove the electronics and put it back with that Velcro. And I intentionally put it right in the middle of the base so that when you position it like this, no one sees the battery, which every kid, all her friends, came and said, battery's ugly. I don't want to see the battery. So this is the wiring. Easy peasy. You just plug in the battery. And then the conductive thread feels exactly like regular thread, so you can tie a knot just like you would with a regular thread. And it turns out two knots is more than enough to create enough of a connection for it to work. So this was a pleasure because I have done soldering before and I'm just not very good at it. But I can definitely tie a knot. So this just shows the thread coming out of it. I ended up with 1.3 meters, 1.5 meters, whatever is a sweet spot. Because if you have it too short, you can't spread it long enough to cover a doorway. If you have it too long, it won't be able to detect people step on it. And this specific thread. But like I mentioned, depending on the resource you have to fence. So building is fun, definitely. And mileage will vary between kids. It's quite funny. I have two kids, one's nine and this one's six. And my older kids, she's not as much into this stuff. But this girl, you give her a power tool and she won't stop. So your marriage mileage will vary. But definitely what I find generally with my kids is see where it takes you. And when I started, I started really simple and I let her kind of drive it. And she really liked the idea of making the wooden base and cutting it and measuring it. She spent ages with the ruler and everything doing it. So that's it for part one of the talk. We'll go straight into the code. So the computer is running circuit Python, which is for micro-python. Like I mentioned, just plug into the USB port of any Linux Windows Mac. And they've basically implemented like a USB flash protocol, whatever, through the computer. So when you connect it, you don't feel it's just like a drive that pops up with one file, main.py. Just open it, any text editor, save it. Every time you save the file, it's so smart, this thing. Because I'd worked with other micro-python hardware and this was like, wow, these guys really went all out. Every time you save the file, it detects it, reboots the whole thing and runs the new version. So you don't even have to do the usual of save and run. And you just press save and it runs it, which is really useful because maybe not for my six-year-old, but let's say you're with a 12 or 14-year-old, they can just plug it in and see examples online, copy paste them, press save each time and it runs. They don't have to figure out all this other software. Now, making this whole thing, I didn't need the REPL. And if you want to do this at home with anyone or at school, you don't need the REPL. But the REPL is definitely very useful because if you want to debug or work with it or teach, you can. And if you want a REPL, you need a little more software to help you. So if you're running Linux or Mac, screen is built in and you just write screen of the device name in the bottom right, and then you have a REPL. If you are on Windows or you want a GUI, you can use this new editor. It's a Python open-source text editor that supports added fruit out of the box and you just press the REPL button and you're set. So let's look a little bit at the built-in APIs that come out of the box with this because this is what we'll be calling in the code. So just want to keep you guys familiar. One import statement and everything is provided through that module called CPX. And so when someone trips over the alarm, you heard the wee woo, wee woo alarm, right? So that's just playing a high tone and a low tone and that's how we'll pay like 800 Agahertz for half a second. You can control the brightness. You can set the colors of individual pixels. So it comes with 10 pixels in a circle. Or you can send this fill which says, set them all as one color and we use both of these. The rainbow wants to send individual alarm, just wants to make everything green or everything red. And then there are two inputs that we want to check for. The switch, there's a physical toggle switch and depending on which side you toggle it, it will either do rainbow mode or it will do intruder alert. And if we have time, I'll show the rainbow mode too and you'll see these beautiful little colors here. And so we check for that. And then finally, the main thing in the intruder alert is checking that someone touched that connection, the capacitive touch sensor. It comes with, I think, 10 or 13 touch pads. And so you can do all sorts of stuff like a little touch piano with fruits and stuff like that. There's tons of guides for this doing it only. I just need one, so I randomly selected A2 pad and that's what I'll be checking for. And both of the switches, true or false depending on if it's on or off. So this is the main function and we will see the other function soon that it's calling. But basically, for readability, I didn't want to go and say set color to 2500. So I created these data structures at the top. One is tones. These are the two tones I use, low and high. I just selected some tones that sound well, like for the siren. And then these are the main colors that we'll be using. And then Cycle, we'll just choose when we rainbow, we'll go from cyan to blue to magenta to red. So that's the sequence we'll use, which I selected with my six-year-old. And she specifically said, no, no, no. That color has to be before that color. So that's her personalized preference for rainbow. Then I'm frequently calling the CPX play tones the frequency or whatever, so I made a helper function called beep that generally would be higher or low. And so the main loop. So this, all the code is one file, main.py. And whatever you run it executes, you don't have to make a main function, but it's just how I code often. So main will call main, right? And it sets the brightness to maximum. And then it will make a beep. That beep is very important. Don't underestimate it, because I turned it on many times and nothing happens. And I'm wondering, is my code running or not? So I always turn it on and wait for the little beep. And it's instant, so I can show you. But that gives you a sense of how long it takes to just start to your code. And it's essentially immediate, which is good. And then we go to an infinite loop. And depending on which way you put the toggle switch, either call a rainbow for the rainbow effect or detect to do the intruder alarm system. OK, so this is what rainbow looks like. So essentially, we go through those LEDs and we go and make them all red, then make them all green, then make them all blue. And this is what it looks like in the dark. And you know when I did this, this is a common example that they have or whatever. And I thought, whatever, but my kids really love this. And what they did is they took it, went in the room, turned the lights off, turned it on, and sat in bed. And essentially what happens is these LEDs go through the whole globe, and then they create this pattern on the ceiling, which just rotates, which looks really cool. So it turned out to be something that they liked when they don't want to play intruder alert and they just want to put it to decorate their room. So that's the rainbow function at the bottom. Constants are the same. And essentially, we loop through each color. And then for that specific color, we loop through each of the LEDs, set the color, sleep for 300 milliseconds. When we don't sleep, it just goes, whee! You don't notice anything. So we need to pause so human beings can perceive the change in color. And so that's the rainbow mode. This is the intruder alert system, which now you don't need a video, since you had three gentlemen give the crawl demonstration. But at home, usually we play this game barefoot and people don't crawl around. They just walk around. And so the distance turned out to be quite good because it's enough to fit any doorway or hallway. So they'd go and set the trap up. And then they'd be like, mama, mama, mama, you have to come to my room right now. And then mama comes and I'm like, wait, wait, wait. What do you want? I'm busy. I don't know what these games are you're playing. But they really liked it because it was portable. They can turn on and off. It doesn't need any setup. Adult go away. I know how to play this game I pay on my own, which is always a plus. So let's look at the intruder code detection system. So it's essentially two functions, calls detect. This just keeps looking, looping, and looking for someone to step on the conductive thread.  So it's like showing that I'm watching in green mode. And then if you trip it, it goes red. So first I did green, slept for 500 seconds, and did black. And then I realized when I was stepping on it, it wasn't immediate. And I'm like, oh, yeah, no, no, no surprise. I mean, I'm delaying for 500 milliseconds. So you need this to be really responsive or else it's frustrating if you're pressing and it's not striking the alarm. So essentially what this loop is, is I'm operating 20 frames per second. I see per 50 milliseconds. So I keep checking that sensor every 20 times a second, which is more than enough to perceive humans as immediate. And then I will show green for 10 milliseconds. So we're 10 frames. And then off for a 10 frame. So that's what it's doing, like 10 green, 10 off, 10 green, 10 off. And that's a full cycle. And then I just check, did someone step on the center or not? If they did, then call the alarm. And the alarm does three red flashes. And then it just goes between high or low. And when you do that, that's how you achieve the sign. Like wee woo, wee woo, wee woo. And those are the two tones, say 960 hertz and 800 hertz. That's what felt natural. And so that's the end of the coding. The last part of this presentation, my friends, will be the teaching aspect. This was very cool and surprising. I didn't expect it to be so fruitful. So basically, I got my daughter. I said, I use Linux. Deal with it. There's no GUIs today. And I took my little terminal, made it full screen, ran the REPL, and said, we are going to control this computer. And I said, write 1 plus 1. And she said, I can do that. And she did 1 plus 1, enter, and 2 came out. And then she got the general gist of it. And I told her that, look, this is the REPL. Whatever you write, you're telling the computer to do. And that's the answer it gives you. You're having a conversation with this machine. Tell it what you want it to do. So then she was like, I know, 1,000 plus 1,000. And I was like, let's see what happens. And we got 2,000, which she loved. And then she's like, I know how this thing works. And so we started off with math, which was good. And then some aspects about doing this with the 6.0. Because I taught many adults before coding and programming, but not that much with kids. So she's 6, right? So typing can be tiresome if she has to type a lot. So what she ended up using a lot is the command history, which is supported through the terminal in the REPL. So you'll see later when she runs beep, she would just, I tell you, you don't have to write beep. You can just press up, and it goes to your history. So if you press up, enter, it will beep again. This, of course, turned into her going up, enter, up, enter, up, enter, up, and I was like beep, beep, beep, beep, beep, beep, and I was like, OK, you get it. Can we continue? It's like, I'm not done yet, up, up, up, up, enter. So that's a very useful feature that we use all the time. But for kids, it's like a lifesaver. The other thing is I'll show you an API created to reduce the amount of typing she has to do. And then the last thing is, you know what? I had this experience when I was a kid, writing that print hello, and then doing it 10,000 times that I figured it out. I'm doing it on my own. And once she figured out how to control the lights, she essentially pushed me aside and said, I'm going to make a rainbow. And she chose each color in each slide because she knew how to do it. So generally, I think this is feasible enough where they can pick it up and then figure it out there. And then we use this to learn about sound and light. So let's see some of this. So now I talked about the built-in APIs, which are very verbose and fine for adults. But for kids, it's a pain to write all of those funny characters, like square brackets and whatnot. So I tried to make it really simple. And so I wrote this little teach-like library, all the code for all these projects on GitHub, and you'll see the links later. Essentially, there's just two functions that she needs to call beep or LED. Beep for sound, LED to control the lights. And if she just writes beep, then it uses a default frequency and specific thing. But then she can write a specific frequency and choose, I want to do a low beep, a high beep. And that opened up the whole discussion of hearing range of humans and dogs. And we'll see that shortly. And then when you are using this directly, these lights are super bright. They will blind you. So you could control the brightness. Very useful. So I just go in and set the brightness to 2%. And that's more than enough, and it doesn't blind me. So I recommend doing that when you do session. Then LED, if you just call LED any presenter.  We just press Shift 0, then Shift 9. But once she figured it out, she was fine. Enter, then the first light will turn white. Then I said, oh, press up again. And now instead of writing white, red, and 1. And now you can set the second light, as red, or green, or blue, or whatever. And then in that way, she just used that kept pressing up and changing the color names and doing that to set a specific arrangement that she wanted. Then I opened it up a little bit and allow the color to either be one of these known names, or hex code, or this little funny notation I made, ones and zeros, computer style. So I intentionally decided not to put a hash in front of the hex code so that she would have less detail. When I taught her about sound, it very quickly turned into, I told her, put a high number, and she couldn't hear. And I was like, yeah, we can't hear these really high frequencies, ultrasound. But dogs can hear stuff that's higher than we can. So we googled, and we found this nice chart showing these different animals in their hearing range. And she just loved this. And she was really curious about, what? A dog can hear something that we can't? But what can we hear that that animal can? And you know, where do we kick animal butt? And where do they kick our butt in terms of hearing? So then we moved on to light. And this was just a wonderful surprise. So I had this USB microscope laying around, cost 30 pounds. I just bought it on Amazon many years ago.  So it turns out that these neopixels are big enough that when you put this using this regular consumer microscope, you can see the individual pixels, the red, green, and blue. What was cool about this is this is a video. So I put it there. We're seeing a live feed of the colors. And I say, give me magenta. And it's only blue and red. And they're like, oh, cool. The green's off. And so when she would play with it, she felt in a very real sense that this idea, that visible light can be represented as red, green, and blue isn't just some theoretical notion. I can physically see these three separate lights through the microscope, but then look and then see how they blend at that level of magnification for a human. So I really recommend this exercise. It was good. Then I showed her a color wheel and said, this is a color wheel. I used the GIMP to free image editor. I said, choose any color. You have 16 million options. She loved all those options. And at great lengths, she said, no, no, no, no. I want this color. No, no, no, no. I'm going to change the shade and everything like that. And then once she chose her color, finally, I told her, look at the red, green, and blue. So it seems that purple is very red and very blue and not so much green. And then I just take that color, copy paste it, and put on the circle playground, then her head exploded. She's like, that's the color I chose. And I was like, exactly. Isn't that amazing? That is a unique representation of one of 16 million colors. And then she very much was like, I like this hex thing. I don't know what it means, but it's very useful to me. Many more colors are available to me now. So I think make hex useful. So this is also fun to do. So all this just happened after this. I grab her and I was like, hey, you want to learn some more? And she's like, sure, I don't have anything. So it just turned into a building on this. When she gets bored, she comes and says, let's do a project. So I throw stuff at her. And so what did I do? I printed this Venn diagram, red, green, and blue. And I said, these are the colors. And all these colors he knows about on the board. And you can tell him the colors, and it will go that way. And then this was an empty table. She filled the whole thing. And what I did is I talked her through the first few colors. So I told her, remember the microscope?  They want to talk about ones and zeros, because they're silly that way. And I told her, on is one, and off is zero. That's all you have to memorize.  And I was like, yeah, you don't have to write on and off all the time. All we want is the zeroes at once.  And she was insistent. But then she finally compromised and said, fine, I'll do it without the ons and offs. And so I made her fill all of that. And then she got yellow wrong. So I said, you're wrong. Yellow's messed up. And so then I told her, I said, you know, figure it out. And she went in, and it was really nice as a Venn diagram, because she could just visually trace it. It says, oh yeah, yellow, green, oh yeah, it's red. And then she corrected it easily just by looking at that. So then after that, we said, let's do the hexes. Remember the hexes? Hexes are easy. Just one is ff zero, zero, zero. So she just went like that, and she did a few of them. She got one wrong and corrected it. And then I said, now let's go back and tell him. Right, you know, one, one, zero, see what happens. And it was yellow. And I was like, see? So basically the lesson that she took out of this was that the same piece of information, like a color, can be represented in three completely different ways.  The end. So now this is my personal website, model 1.0. That's an O, not a zero. And on it, you'll find the slide, the codes, the links to all the products and everything like that. And I open up to more questions and answers. Thank you very much. So now, do we have more questions? Yeah, we do. Thank you for a brilliant, funny, inspiring talk. What's the next? Sorry, wait, where are you? Cool, sorry. It was like, the voice is coming here, you know. What's the next techie project you got planned for? Oh, great question. So musical paintings. It's all from this Adifruit website. They have really great products. They have this electric ink, it conductives. So we're going to make a painting. And depending on where you touch on the painting, there's a circuit playground stuck to the back of the canvas. And it will go and play music and pop songs like Havana and I don't know what is their choice. But you press on different parts of painting and do this, that's our next planned project.


From San Francisco, it's theCUBE. Covering OSISoft, PyWorld 2018. Brought to you by OSISoft. Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at OSISoft's PyWorld 2018 in downtown San Francisco. They've been at it for decades and decades and decades, talking really about OT and efficiency. And we're excited to be here, it's our first time and really want to talk to a customer. Excited to have an ex-customer, CJ Smith. She's a project manager for the city of Riverside. CJ, great to see you. Thank you, hi. So you represent kind of a whole slew of kind of mid-sized US city. So how big is Riverside for people that aren't familiar? We serve 120,000 customers, so we're not too small, but we're definitely not as big as some of the other cities. Right, and then as we said, before we turn on the cameras, you guys have a whole department for utilities. You have your own utility as well. Yes, we do have a public utility division within the city, also an IT and public work, parks and recs, like other cities as well. But we do have the utility, which is different than some of the standalone utilities, you know, like LADWP, for example. Right, right, but it's good you're saying off camera that that gives you guys a nice revenue source, so it's a nice asset for the city to have. Yeah, the utility is revenue-generating department. Okay, so what are you doing here at PyWorld? How are you guys using OSI software? So we started down in Py back in August 2016 as an enterprise agreement customer, and you know, at that time, we really lacked visibility into our system, so we needed something to help us kind of gather the data and make sense of it, because we had data all over the place and it was hard to answer simple questions, it was hard to find simple data, and so we started down the Py journey at that time, and we basically used it like a data hub to aggregate data, turn that data into information, and then we disseminated using like dashboards, so PyVision dashboards, which used to be PyCore site, and as well as reports. So what were some of the early data sources that you leveraged, that you saw the biggest opportunity to kind of get started, or you had even more importantly, kind of your earliest successes, where did your early success come from? Yeah, so our very first work group that we worked with was our water operations and our water skater team. Seems to be a pattern here, a lot of water talk here at OSI soft. Yeah, I'll talk about electricity too, but we started on water and the first thing we did was implement their data, it was called a water operations dashboard, and they were doing it manually in Excel, and it would take a staff person over eight hours to do it, and they would do it the next day for the previous day data. So imagine how opposite of real time that is, right? So we integrated that data with Py. And how many data elements?  Yeah, this skater, so the water skater tax that we brought in were near 1,500 tax. So you imagine that much data and calculations with over 1,500 calculations behind it. So it was a ton of effort, you know, and a huge quick win for them. So it saved staff time, they now have actionable intelligence, real time data, the managers get alerts to their phones about the status of wells, and so it was really helpful to that work group. So that one was one of our first and earliest wins on Py. Was it a hard sell? So those people to use it? It wasn't because we did find a champion in that group, someone that would help us, actually the manager, he was very interested in technology and automation, and they understood that even though it would be a time investment upfront, it would save them a ton of time in the long run, you know, for the rest of the year. And so one of the things that helped us get buying early on is that we use an agile approach. So we would tell the manager, I only need you for five weeks.  We will deliver the product in five weeks, we will do all the work, but if you could give us five weeks of your time, then you know, you could have all your time back the rest of the year and that helped us get, you know, buying from the managers and a commitment because they can identify with, okay, just five weeks. Right, so those are probably the operational folks. What about on the IT folks? How's getting a buy-in from the IT folks? So IT, the funny thing is, and the thing we did different is we have a great relationship with IT and we really forged a partnership with them early on, even from the very beginning when we were just reviewing the agreement. We got there buying early on to say, okay, this is what we're thinking about doing. We want you to be part of the team and we really built a partnership with this project so that it could be successful. So they work hand in hand with our pie implementation team every step of the way. They've been on this journey every step of the way with us. So we don't have some of the challenges that other companies that I hear talking a lot about here with IT and kind of being a bottleneck. We didn't have that same experience because we really worked hard up front to have the buy-in with them and really build a partnership with them so that they're implementing pie with us, you know? Another selling point with that is we're using pie as a data hub or like a bus, a data bus, essentially. So for them, it's good because we're saying, look, we're only going to have this one point to point system instead of having all of these individual points, we're only going to connect to one system which will be easier for them to manage and maintain and will instruct staff to go to pie to get the data. So that's a selling point for IT. It's more secure, it's more manageable. And did you use an outside integrator or did you guys do it all in-house? Our implementation team is a combination of in-house staff and a consulting firm as well. Okay, okay. And then it's curious, because then you said once you had all the data, it's kind of a data bus, how long did it take for somebody to figure out, hmm, that's pretty cool. Maybe there's data set number two, data set number three, data set number four. Right after our first six-week implementation, we rolled out a new implementation every four to six weeks. Every four to six weeks. Yeah, so we did a sprint cycle the whole first year and actually the whole second year we're currently in right now. And so we touched a different work group every single time, delivering a new solution to them. So we've picked up a lot of traction, so much that now other departments in the city want it. Public Works is asking for it, the city manager's office. So it's really picking up some good buzz and we're kind of working our way down discussion of smart city talks and seeing how pie can support smart city, big data, advanced analytic initiatives at the city. So what are some of the favorite examples of efficiency gains or savings that Department A got that now Department B sees and they want to get a piece of that. What are some of your favorite success stories? I would say two of mine. I shared one on the big stage yesterday about the superpower. I talked about our operations manager who started receiving action intelligence overnight and he got an alert around midnight and he called his operator and said, hey, what's going on with that well? And the operator said, very puzzled, how do you know that there's something going on with this well? And he replied and said, because I have superpowers. And so his superpower was pie and that's one of my favorite stories because it's just simple and it resonates with people because he is receiving alerts and push notifications that he never had before to his mobile device at home. So that's a huge win. Was the operator tied into that same notification or did that person know before the operator? The manager knew before the operator. So the operator didn't know about pie at the time and we had just rolled it out. And so the manager was just kind of testing it and adopting it. And so it was kind of like, he had a leg up a little bit and they were confused like, how do you know you're at home? He's like, I have superpowers. It's probably my funniest and best story and one that I always tell because it helps everyone, no matter if it's an executive to a field person really understand the power behind pie. I think another one, if I had to pick another example of a win that I think was powerful is our work order and field map. So we have our field crews right now that have a map that's powered from our work order and asset management system pushing data to pie, which then pushes it to Esri through the pie integrator and they're out using it in the field and it helps them route their work. They can see where their workers are. They can see customer information and that map is really changing the way the field crews work. So like imagine a day before the system where they would go in and have to print every work order from the system. And not all asset management systems are really user friendly. They're kind of archaic, a little clunky. So I want to say the name of our system. And it doesn't work well if there's a change, right? Yeah, and they're not really mobile friendly, you know? So that's part of the challenge. But because of that, now public works wants that map, parks and recs. You know, every department that has field forces, they want something similar so that they can be, get all the data from all the other systems in one app, in one location on their device. And do you find that that's kind of a consistent pattern where often department A needs very similar to what department B needed with just a slight twist. So it's pretty easy to make minor modifications to leverage your work across a bunch of different departments. Yeah, absolutely. A lot of work groups are similar, maybe a little different like you said, but especially those that have field forces. It's, sometimes it makes it easy to sell it to the next group. You know, it's like, look, this is what we've done. Is this something that you kind of need? Or what would you need differently? Like we've developed, you know, field collection tools. That's easy to replicate. Once you see it, it's easy to say, you know what, that works, but I need it to say this and I need to say this. If you just show them a white paper, it's hard for them to say, this is what I need. Right. You know, most people just don't know, but it's easy once you see a suit to say, oh, I don't like that tie, I don't like that shirt, I don't like those pants. But something close. Yeah, but something like that, right? Right, right, right. So that's the benefit of once you start having a solution to easily modify and really reproduce and then the good thing about agile, you're running sprint, so you're learning every sprint. You know, you're kind of learning as you go and you're able to refine it and refine it in, make the process that much better, you know? Right, just on the superpower thing, it just, you know, customer, or not customer, but employee retention is a challenge. You're not getting good people as a challenge. I'm just curious how that, you know, kind of impacts the folks working for you that now suddenly they do have this new tool that does allow them to do their job better. And it's not just talk, it's actually real and gave that person a head up on the actual operation of the person sitting on the monitor devices. So, you know, as it proliferates, what is the impact kind of on morale and are more people kind of rising up to say, hey, you know, I want to use it for this. I want to use it for that. Yeah, we are getting a lot of interest and I think people, you know, the challenges, and I talked about this a little bit during my session, is like change management and culture, right? Some people see automation and technology as sometimes a threat because of job security or, you know, the I've always done it this way type of mentality. Never a good answer. Right, but once you kind of get them to see that, we're just automating your process to make it better so that you could do more cooler things, like better things so that you can actually analyze the data instead of inputting data, you know, so that you can actually solve problems versus spending all your time trying to, you know, identify the data and collect the information. So, we're definitely, staff are starting to see the value and after the first year and a half, we've gotten a lot of traction. I don't really have to sell it as much. It's now such a huge part of our culture that the first question is like, when we want to implement a new system, is does that integrate with pie? I mean, that's the first, so I don't even have to ask them. Everyone else is asking, well, have you thought about using pie for that, or can pie, so we always kind of look to pie first to say, can we create the solution in pie? And then, if not, okay, then we look at other solutions and if we're looking at other solutions, we say, does that solution integrate with pie? So, that's become part of our norm to make sure that it plays nice with what we're calling our foundational technology, which is pie. Right, so you've talked a lot about kind of departments. Is there kind of a cross department city level play that you're rolling data and or dashboards into something that's not, you know, that's a higher level than just the department level? Yeah, so far, the only thing that we have done that's kind of cross-divisional, not just in one division, is our overtime dashboards. So, we recently created overtime dashboards throughout the entire city so that, you know, executive level department heads have visibility into overtime, which just gives them trends so that they can know, you know, at what departments are receiving the most overtime. Is that overtime associated with, you know, what type of cause? Was it something outside of our control? Was it unplanned overtime? And then most importantly, where are we trending? You know, where are we on track to be by the end of the year, given our current rate so that they could be proactive in making changes? You know, do we need to do something different? Do we need to hire more people in this department? Do we have too many people in this department? Can we make shifts? So, it's giving that level of visibility and that's a new rollout that we just have completed but it's something that we're already seeing a lot of interest in doing more of cross-divisional things so that the city manager's office in that level has more view into the whole city. Right, right. Well CJ, it sounds like you're doing a lot of fun stuff down the river side. We are, we are. And you can never save enough water in California so that's very valuable work. That's true. Well, thanks for taking a minute and sharing your story. Really enjoyed it. Thank you for having me. Absolutely. She's CJ Smith. I'm Jeff Frick. You're watching theCUBE from OSISOF PI World 2018 in San Francisco. Thanks for watching.