If any of you have ever taken a writing class in your lives, you will have heard this phrase, show, don't tell, right? It's the number one thing that the especially creative writing teachers to say to their students, comment that you'll see all the time in your assignments or your essays, show, don't tell. I don't want you to tell me about what the character does. I want you to narratively show it, right? This is relevant to me in particular, obviously, because we're talking about data and storytelling, but my PhD is in literature, it's in storytelling. It's in understanding how a story works and how you can convey all sorts of meaning through story. Data is just one of the tools that we can use to tell really good stories. So today we're gonna talk about how, much like any other kind of story, data can be used to reveal a tremendous amount about your work, your impact, your community and that some of the same principles of storytelling when we're using text and words apply to when we're using data. So, yeah, let's jump into it. So just a second in Eli's land acknowledgement, I'm based in Toronto, the first generation settler of Irish descent, the place where I'm in, known as Toronto is covered by Treaty 13, it's the territory of many nations, including the Mississaugas of the Credit, the Anishinaabe, the Chippewa and the Hamasani and the Wendat. It's also now home to many diverse indigenous peoples. And as a person whose home Ireland is still under colonial rule, I deeply support the rights of all colonized people to assert their autonomy in the face of settler states. This is a beautiful image of beadwork by a collaborative of indigenous artists. I just think it's just a stunning piece of art to represent how many different nations this country is a part of. So yeah, just briefly about me, as Eli mentioned, I'm an accidental techie, which is to say that my training is not in technology or data science. I came into this work because I had really great mentors along the way. And because I was just curious about how things could be done as effectively and as sufficiently as possible. And it turns out technology is a really big part of that. So I, as Eli mentioned, I've played every conceivable role in a nonprofit and I now I'm working, I would say nonprofit adjacent. I work as a consultant with small nonprofits. Organizations usually less than a million dollars in size. Those folks who don't necessarily have an IT department and I become a little bit of their IT strategy provider and we work on getting them really technologically enabled. So I've been telling stories about data for a long time. This is just a bunch of various videos about my work with nonprofits to use data and technology better in their work. You can check them out at onyamaclin.com. Also fun tour through my hairstyles over the last decade. It's always fun. So what's on tap today? So first we're gonna talk about what kinds of stories you can tell with good organized data. We're going to talk about why audience matters the most when you're thinking about storytelling, particularly or yeah, particularly when you're using data. How do you apply structure to your data to tell stories? A lot of you have, I would say for the most part unstructured data, okay? And in order to really sense make we have to apply structure to it. So how do we go about doing that? How do we determine what kind of structure to apply to it? And then some practical steps that you can take today to serve for stories in your data. Some really concrete things that you can do right now after this session, if you're feeling motivated to start to unpack the data that you do have to see what potentials there are there to tell stories. And of course also to see what kind of data you might be missing and going forward with to collect. As I go along, please feel free to pop any questions into the chat, especially if there are questions clarifying something that I've just described. Eli will let me know and I'll be happy to provide any insight or additional definition to help clarify. But then yeah, if there are questions of a more substantive type about the content then we'll certainly leave some time for them at the end. So where we're trying to get to in some senses is data that can be accessed by the end user and that they can basically use a self-serve approach in order to answer the questions that they might have about your data. So it's really best served and shared in dashboards that can be manipulated by that end user. For example, I was working with a client who had a fundraising board. So this board was really engaged in fundraising and that board wanted to see the evidence of their efforts and they could be writing emails to the executive director and saying, okay, where are we at? Have we gotten donations? Any new grants from that email that I wrote you? Where are we? And that can be quite challenging for an executive director or a director of development to have to manage those information requests. So instead we thought, what if we can make a dashboard that will reflect that donation and pipeline data in real time and that the board members can just access on an intranet and they can run their own analysis because each of them have their own questions about the effectiveness of their particular effort on that campaign. So I'm just gonna open this up really quickly and just show you what in terms of self-service. This is just an example again of when we think about who the audience is, in this case it's our board members and we think about what kinds of questions that they might have, what kind of stories do they need to tell about their work? Then we can come up with some solutions that help them answer those questions without us necessarily having to be hands-on, okay? So this is obviously a little bit of an exercise in user design, user-centered design and then obviously an exercise in data. So just really nice that if I'm a board member, let's say I'm Eliana and I wanna see, okay, what's been the impact of my effort, right? So now the charts all change so that I can see, okay, there's 38 donors that gave a gift as a result of my efforts. These were the sources. This is the average donation year over year. Let's say I just wanna look at 2019 gifts. I can do so, I can see the value of the gifts that I've made that have been made as a result of my efforts. So this is just an example of one of those kinds of client dashboards that we put together and that really help the end user answer the questions and tell their own stories either to themselves or to perhaps other board members imagine Eliana being able to say to other people on their board, hey, I'm kicking your butt this month, right? Like maybe you need to get on that pipeline building that's in the same way that I am because look at the results, right? These kinds of database tools help people be compelled to be motivated to continue their efforts. Studies have really shown how much, how simple we are as humans in some ways, we really like to see evidence of our effort, right? So if we can use data to show people the story of their effort over time, they're much more likely to continue along that road to that effort. And this is just another example of, again, using data organized in the visualization tool. This is just a Google sheet, by the way, that allows the end user to tell their own story, to use the data to tell whatever story they want. So this is a program where the organization is dealing with a lot of post-secondary students who they're coming in and working with them on future skills development. So the client is able to, let's say, just look at their last three cohorts, what was the program status and completion. We can see we've got 60% completion in there. We've got slightly more urban students than rural. We have 16% of those students identified as having a disability. So the organization, because we've organized the data in such a way that it's self-serve in order to help them use that data for whatever kind of story they need to tell to whatever member of their community they are engaging with. So the idea really being that when we create data visualizations or collections of data that we make it obviously filterable and manipulatable for the end user because every user will have a different way that they want to use that data to tell a story. So it's a really key first step when you're thinking about leveraging data for storytelling is recognize that the stories are infinite, the audiences are infinite. And so showing my data in a way that's easy for folks to be able to use how they need to use it is really critical exercise in user-centric design. One just additional way that you can use, so this is a specific example of ways that you can use data to tell a particular kind of provocative story is this sheet. What it shows is event costs for an event-based program. Okay, and this program has decided to track expenses related to the events that come from social enterprise vendors, okay? So every time that there's a purchase they use art in their event. Every time there's a purchase from an artist or an organization that's a social enterprise we track that it was from a social enterprise vendor. Similarly with event costs, marketing and promotion materials and supplies of printing. I did a lot of printing for this event and always use social enterprise printers. So we were able to then determine that 12% of our total event costs were spent with social enterprise businesses, okay? Now, this information was something that we collected because we were interested in. But what it ended up being really useful for was telling a story to the funders, many of whom were investing in social enterprises or had a social innovation focus. And we were able to say to them, hey, you know what, when you give us a dollar for our program, that money's going back into the community because we're prioritizing spending the money on social innovations. And look, we can show you our event budget data right here. We could pull it up in the meeting and show them we're really committed to this, okay? So we use this data to tell the story of our commitment beyond just the outcomes of the program but into the whole social innovation space writ large, okay? And so the data's been really provocative in that story because it doesn't really lie, especially financial data. Listen, there's all sorts of examples of people who fudge all sorts of financial data but that's not what we're talking about here. We're talking for the most part, the dollars and cents don't lie. And this effort to track and represent the data in this way indicated to the funders the intention and the value of the organization. So it conveyed a value proposition. It wasn't just, it wasn't really about the facts of the figures, like they didn't really care that it was 12%, that could have been, that number could have been 22%, that number could have been 8%. But what it demonstrated was that we have intention around this because we put effort behind it, okay? And that is what creates trust and that's what creates the conditions for additional support. So, you know, all that to say that in some ways data-driven storytelling really does inspire trust, okay? For better or for worse, quite frankly, because we know that data can be poorly manipulated or manipulated to nefarious ends. So for better or for worse, data-driven storytelling does tend to inspire trust, okay? But in order to get to that place of trust in using data to tell our stories, we have to know something about the audience for whom the data story is intended, okay? Before we can know how to inspire trust in them. In the example of that funder, again, we know, again, that they are invested in the social innovation space. We know that they support some social enterprises. So we know that they value social enterprises. So us showing them our commitment is that a tactical way to use the data to tell that kind of story and create that kind of connection. But we had to know those things about them as our audience in terms of them being our audience before we could make that leap into what kind of data-driven story to tell, okay? Listen, all stories have audiences. In fact, you could even say that a story doesn't really exist without an audience. Again, if I can recall back my literature at degree days, again, I had one fantastic professor named Ted Chamberlain who wrote a book called, If This Is Your Land, Where Are Your Stories? Which is a fantastic book about storytelling and land and place. But one of the things that Ted was an expert in was in the oral storytelling tradition. So the pre-written tradition of telling stories. And what he described was that we used to tell stories with the intention of them being remembered, right? Because if there's no mechanism to write these stories down, then you have to ensure that you're telling a story in a way that it's gonna be memorable, it's gonna be repeatable, and that the person who's listening to the story, even though they might put their own little spin on it, is gonna remember the core elements, okay? So we actually have this built-in wiring in our brain that is connected to story, whether that's telling it or listening to it. And that's our tradition of telling stories as humans is much older than our written tradition, right? So it's really deeply agreed in there. But again, in order to tell that story effectively and to tell it effectively to be remembered, you have to think about your audience. Who is the story for? Okay, so whether the story is fiction or it's text or it's data, you have to think about your audience. What do they care about? What information do they need to know? Who are they? What are their priorities? Okay, so these are the really the first things you need to think about when you're starting to think about using data to tell stories. A really good method. And those of you who are marketers on the session, this will not be unfamiliar to you. You will probably know this paradigm. So it's the no-field-do method, okay? So again, your data story isn't about the message that you send, but the one that's received, okay? So how are you ensuring that message that you send is received in the way that you want it to be? And that's an exercise in getting into the mind and into the values and interests of that audience identifying them. Just again, using this example, I just did the no-field-do. Okay, so who is this audience? Their private foundation with an interest in social innovation. The no section is articulating the point. Okay, point of the story. And the point of our story here is that our program is committed to supporting social enterprises. The field section is an effort to show the audience that you care about their priorities, okay? And so we did that by saying we track every dollar invested in social enterprises. Your support for our program is also support for social innovation. Okay, so that's showing them that we care that their priorities are our social innovation and social enterprises. And then the do section is how do we be clear about what that audience should do next? So the next is look at the data, review it right here and see where we spend our money, okay? And as a result, then feel confident in supporting our organization going forward. So now that you know your audience and who you're going to be telling this by your data-driven story too, now we need to get to know the data. And this is always a little bit trickier than getting to know the audience, I think, because it requires to some extent some skills that you might need to bring in some analytical thinking, some process-based thinking, some folks who are really good at categorizing things. If there's someone on your team who always orders stuff on their desk by small, medium, large, or who organizes their books in alphabetical order, might not organize by genre, or by color, that person who's always doing the organizing, they're probably a person who you really want to bring into this exercise of getting to know your data because they probably have a little bit of a process-based or organizing a tendency. So somebody might be familiar with the data information knowledge, wisdom pyramid, okay? So this is really important to consider as you think about getting to know your data. So at the bottom of our period of our pyramid is the data section, right? So this is received from the world more or less as a reflection of how the world works. It's a drastic oversimplification, but essentially it is to some extent a representation of some kind of truth. So for example, if I were to pull out a thermometer and measure the temperature in the room, I would have a measurement of the temperature, which would be my data point, that the temperature exists, regardless of whether I'm measuring it or not, my act of measuring it is what turns it into data, okay? So that data comes at us from all different places. If we choose to collect it. But in order for it to become information, this is where we have to apply some structure to it. If I just put the number 18.5 on a piece of paper, it would be just the number 18.5. But if I put a little bit of a C next to it, then oh, that's degree Celsius, okay? So I've applied some structure to it and now it's become information. So now you know what the temperature that number is referring to temperature, okay? The next step is, again, another human, I guess human intervention to turn that information into knowledge. So the human intervention is, okay, 18.5 degrees. I think I only need a light jacket, okay? So with that information, now I know what kind of jacket I need to wear, okay? And then again, the wisdom is again, another human intervention that applies another layer of knowing to knowledge, okay? Which would be the wisdom to know that generally this time of year, that's the kind of temperature, right? So that's the level of wisdom that we apply along this example. So you can't get to any kind of wisdom or knowledge, which we typically try to convey in storytelling without first knowing what your data is and applying structure to it so that it becomes information. Again, if we go back and think about our social enterprise spending budget, we had to apply that structure to our budgets, which is to say, just a column that says, is this a social enterprise friend or not? And if it was yes, then that was enough structure to turn that data, that budget data into information about our social enterprise spend. So then the question becomes, what logic should you be applying to your unstructured data? Again, the data has to have that structure to be used in storytelling, otherwise it's just floating around the bottom of the pyramid with no way to move up that pyramid, okay? So these are just some examples of structured versus unstructured data. Structured data, some examples, a date, a phone number, social security, a social insurance number, credit card numbers, names, addresses, transaction information. Usually this information is already structured because it has gone through a predetermined process by which structure is applied to it so that it conveys meaning or information. But unstructured data by contrast, it typically looks like sort of text files, reports, email messages, audio files, video files, images, surveillance. I'm not sure where this randomly chose, they randomly chose surveillance imagery to include in this table. But generally, so like in a report, there might be structured data, but the report itself is unstructured, okay? You can start to apply structure to any one of these unstructured data sources. And that's a process of human intervention. So for example, with your images, you might start to apply tags to those images. So now the images become a bit more structured because you can find them, you can group them, and then you can start to use those groups or those categories or those tags to tell different kinds of stories about those images, okay? But that effort of, for example, just tagging a series of photos means that you have to clarify what it is that's important to you about those photos, okay? So what would be the set of tags that I would wanna use on those images, okay? And that is, it sounds like a very small and pedantic exercise, but it's actually deeply related to your mission-related goals. The effort to apply structure to your data should flow from a well-established and defined set of mission-related goals, okay? So if you haven't done so, if you've never really gone through the exercise of defining what your organization's mission is, what you're working towards, what those outputs are that indicate whether or not you're meeting those goals, then this is an exercise that you really wanna do before you can start to dig into what kinds of data you can use to tell what kinds of stories. So this is often commonly referred to as a theory of change exercise for those of you wanting to get really good at using data to tell the story of your organization's effort or impact and who haven't done a theory of change or perhaps haven't looked at your theory of change in a long time, this is an essential first step, right? So it's the story flow, it's the hymn sheet, right? That indicates that we're all singing the same song, okay? And without the effort to define your theory of change, which is just really briefly, it's the big statement about why your organization exists and then it also includes what those outcomes are of your work, okay, so here we've got a program that is working with street-involved youth and their outcomes are that young men are self-reliant and employable, the young boys live in a stable environment and that less youth end up on the street. Then they know these are the activities that we do that contribute to these outcomes, okay? And so all of that is in, all of that theory of change and that flow from activities through outcomes into impact can be understood as effective when we start to look at the data that we gather as a matter of conducting those activities, okay? So this theory of change, this really nicely designed map and flow of activities through outcomes and into impact always then leads to a measurement and evaluation structure, okay? Which is what we have here. So here we have, we have some outcomes, we have an impact area. So for example, if an impact area is sustainable income, so we want our program participants to have sustainable income, then the indicator of whether or not they have sustainable income is if they have a job, right? And the way in which we understand if they have jobs is that we have a source for that data. So we have home visits, we have a home visit form and an aftercare notification, okay? So those are the data sources that contribute to understanding whether or not our program participants are getting job placements and as such having sustainable income and as such not becoming street involved. So both, but the effort to understand our data has to be done in the context of our larger organizational strategy and mission. So telling good stories through data means that we need to know what story that we're telling, okay? And that everybody needs to be on the same page and the theory of change is really that common story flow. Okay, so again, if you're looking to get better at telling good database stories, first you need to understand what is the change that you're hoping to have in the world, okay? So rapid theory of change exercises, there's all sorts of really great free workshops and content online that you can find that will help facilitate just a rapid theory of change exercise that you might wanna go through. So again, so knowing what your mission is and the type of data that you need to collect in order to contribute to understanding whether or not you're delivering on that mission is really critical. So as part of your effort to get there, you can start today by looking at what data you do have, okay? So knowing what kind of data you have, you do have is a good first step in understanding what stories you can begin to tell. So for example, did you have more than 75% participation in a program survey? This can be a really good sign of high engagement. Is your open rate on your email invites to the program really high? Also could be a good sign of good engagement. Do you have participants that have attended multiple events or programs? Also can be a sign of really high satisfaction, okay? We've got, oh, we have 30% of participants have attended more than three sessions, okay? That would be great. So what you're doing is you're looking for the countables in this data. So that you can begin to make inferences about what those figures mean. So by the countables, you're looking for for that kind of quantitative data. So anything that a number can be applied to. So imagine that you're gonna audit program data has a good place to start. So you'd wanna gather together any survey results. Maybe there's an exit survey and an entry survey. You wanna look at your email invites. And if you have that ability to look at who opens them, who clicked on them. You wanna look at event details. What were the events that were associated with the program? Where, what was the location? What was the time of them? What was the theme of those events? Oftentimes we don't do enough to categorize the themes of our content so that we can understand which themes are resonating over other ones. You wanna look, gather your registration forms. There's a lot of good information that you're usually counting the number, obviously the number of registrations that you have, but are you asking additional questions in those registration forms that could have some good data, some good information and relevant data there. Look at your list of facilitators. What do you know about them? All the data that you can know about your facilitators, demographics, where they live, what their areas of expertise are, how often they facilitate your participants, how many of them, who are they? Again, demographics, participation rates, lengths of engagement with your organization. Photos, look at, categorize them by the metadata that they already contain, which is usually a date and a time. If you upload a photo from your phone into a drive, it's automatically named with some metadata, which is usually date and time. Okay, and then can you go through the effort, as we mentioned, of tagging? And tagging is really key. Can you look at your budgets, right? What kinds of program-related budget data that do you have, right? So gathering it all together is really key. Eli, you're absolutely right. Tip, try to know what you wanna report on before you launch a program that's obviously essential, and that would be part of that theory of change exercise. Sometimes though, we lose sight of program reporting and find ourselves with a whole lot of unstructured information, or perhaps we have good information that's contributing into a funder report, and that's the only way we've thought about reporting on our program data, but in fact, what else can we surface? What else can we surface from that program-related data? That's not just about checking the box at the funder needs, but actually connected to our, again, our mission and our values and our output. Yeah. Okay, so once you've gathered all of that data, and now you're like, okay, so I've got all this information, what can I surface in here? So now this is the creative part of determining what kinds of questions you wanna ask of that information. Did participants benefit from the program, for example, which events were best attended? Did some facilitators resonate better than others? Have our program costs increased, decreased, or stayed the same? Why? So coming with a set of questions, you might call this the business analysis piece, coming with a set of questions to apply to that data when you put it together can be really helpful, because then it's this really cool investigative journey, because you're like, did some facilitators resonate better than others? Hmm, I wonder how we could figure that out. Okay, let's cross-reference the dates when we know facilitators ran sessions with the survey data for those particular dates. Oh, now we can see facilitator A, receive scores, eight out of 10 satisfaction, facilitator B is like six out of 10, and facilitator C is like five out of 10. So what do we need to do to support those facilitators to deliver the content in a way that's more successful? So coming with those interesting kinds of questions helps you understand more about what you can do in the future to perhaps improve on your program, or just tell the story of its success. So context, again, is really key when you're looking to analyze that data and surface some good stories. So make sure you're considering the full picture. As we mentioned, data can be used in all sorts of convenient ways, the way to make sure that you're not oversimplifying is consider context, right? So your registration data might look high, but your attendance data is low. Okay, did something happen that led to people not attending? Was there an email reminder sent? Was there, was the link broken? Or what perhaps happened that we got, had such a big drop-off? Another example, your survey data may indicate that some people didn't have a good experience with the program. Are there commonalities with their context that would explain this? Did they all participate on a day when there was a technical difficulty, for example? Make sure that you're looking at the full picture around that piece of data. So next, the next piece to take into account is to be aware of bias. So be aware of what you don't have information on or what's perhaps missing. So the data about those who didn't complete your program, for example, or weren't as successful or weren't as successful is as important as the inverse, as those who were successful. So the solution might not be in what's there, but what's missing. And this image, some of you might be familiar with this story. There was during World War II, there was researchers at the Center for Naval Analysis were trying to protect pilots from being shot down on their runs over Germany. And they were like, let's look at the planes that are coming back and let's see where they're sustaining damage and how we can better protect our pilots by improving the design of the armor on the planes or of the bombers themselves. But one very smart analyst said, okay, but you're only looking at the bullet holes on the planes that came back to base in a sense of the survivors. What about the ones that went down? So in fact, what they realized is that all of the holes on the bombers that came back to base indicated the strongest places on the plane. They indicated the places where the plane was most likely to be able to take fire and take damage and not go down. In fact, what they realized was that if they protected all of the places on the plane where there wasn't any damage that in fact they would ensure that more planes came back. And that's in fact, exactly what happened. The places that didn't have any damage were the fuselage, this, I don't know, I'm not an aerospace engineer by any means, but these were the vault, these were actually the most vulnerable spots in the plane. So that's what they ended up reinforcing. So you have to be aware of what you're not seeing in the data because what you're not seeing can also be where the really great stories can be. I love that story, a great story about bias. And then finally, you wanna categorize your qualitative data. Okay, so like I mentioned, you might have big text files or image files or audio files and you might not know how to analyze them, especially text-based content. For example, you might have anecdotes or texts given via interviews or open text boxes in surveys. So the tip would be read all those responses first and then start to group them into categories. You can even do this as a physical exercise, right? Print off all of those interviews or those text-based anecdotes, cut them up and start grouping them together. And then you can apply a category or a tag to those responses, which again, turns them from unstructured a whole bunch of text into something that's structured that can be found easily, that can be analyzed and be like, oh, we've got 80% of our comments fall into this category, 10% into this one and a 10% into that one, okay? And that effort can help you recognize patterns again of success or challenge or opportunity. Okay, that was a lot. Sorry, I feel like I was just racing through that to some extent, but we've got a few minutes left. Interested to know what kind of data do you have that you're hoping to tell stories about? What questions are you trying to answer with your data? Who is the audience for your data-driven stories? It's really key, remember that one. And then the final one to keep in mind is what structure do you need to apply to your data? Okay, so those are just some key reflection questions for you to take away. And now I'd like to hear from folks what questions make you have. Thank you, I did. That's really great. And I love the really practical approach of saying sometimes we just are gonna have unstructured data. How do we structurify it? Which I think is a really fabulous insight to say, oh, it's not lost. There are still real opportunities around this. I'm gonna have a moment for people to drop questions into the chat. Let us know what you're curious about, what some of your experiences have been. If you share a story of pain and tears. But let me start off with a big question, which is the longitudinal survey question. So I've often struggled to do some of that behavior change questions to say, they started here, then they did cool things with my program. And then at the end, they're probably doing a whole lot better. But what I often struggle with is once people are done with the program, their incentive for giving me the time to fill their survey is really reduced. So I get really low returns on those feedback requests a year later. And I'm wondering if you have any tips on how I might be able to more effectively structure these longitudinal kinds of questions so I can see behavior change over time. Yeah, that's a great question. And it's an issue I'm having with three of my clients right now who all work on programs where they are looking to see a behavior change as a result of the efforts. And that's difficult to measure. And disclaimer, I'm no measurement and evaluation expert. Behavioral change is very difficult to measure from a survey to begin with because people don't tend to analyze or reflect on themselves in a negative way. So if you say, you're never gonna say, oh yeah, I'm deeply prejudiced. And then at the end of this program, yeah, no, I'm not prejudiced anymore. You can only really count the effort that you've engaged with in. And then I think for more of those behavioral change or those longitudinal efforts, I think interviews are some of the best ways to do that. And I think you incentivize people over the longterm. A, by giving them a place again to track their progress. So whenever you can create opportunities for people to have their own little sort of sections of a platform where they can see a progress bar. Oh, you've done this many learning pieces. You've engaged in this many feedback sessions. People will tend to want to stay connected and continue to move along with the program. So I think that can be one effective way. And then obviously we don't have time to go into this and I'm sure there's content that you provide on this. But AI is starting to emerge in ways that can do some really powerful stuff around longitudinal behavior change. I know one organization I was working with that was working around violence in the workplace. And one thing that they played around with was a machine text analysis program that they basically installed over their email client. And it could read, it wasn't reading the emails for content. Like it wasn't interested in what was being said in the emails, but what it was able to surface was sentiment in emails. And they could see, they could then send folks like a little bit of a nudge saying, hey, you might want to think about using this kind of language. But like that language it used was really, it wasn't inclusive and you might want to think about using this instead. So really interesting application of technology to play that role of a little bit of a coach to help reinforce the learning. Yeah. That's really helpful. And I think, yeah, it's always a great reminder that there's this huge gap between people's expressed behavior and interest and what they actually do. What they actually do. And there could be a huge gap between those. Quick follow up question. If we were to go and take this survey approach, which is obviously higher lift, we're not going to touch as many people, but as you say, it might let us get a little bit deeper. Do you think I'm going to harm my results in any way if I would use things like a gift card to incentivize participation? I don't think so. No, I think any kind of incentive that you can provide to honor and respect people's time, quite frankly, obviously we see this in more community based fundraising and research to compensate your research subjects for their time is respectful and completely appropriate. Got a comment coming in here from Michael. Do you want to come on, Mike? Is that possible? Yeah, sure, thank you. Michael. Hi, I'm from Hong Kong and responding to Ellie's question about the self-administrated or self-report behavioral assessment, of course, them will be a problem of a return rate and overall underestimation. And in my experience, we built in multi-parties assessment tool for play therapy in our center. And what I mean by a multi-party that is, we observe the children's behavioral change and also we ask the parents, the mother and father, what they observe at home. And if possible, we'll also send a questionnaire to the teacher to see if the behavioral change is present in the classroom. And what I, my experience is that if the parents are concerned, especially when they are the referral, referring to kids for our service, they are more motivated to complete the questionnaire. Yeah, that's really good insight, Michael. Thank you for that. Yeah, as many people as you can have answering the same question from multiple different viewpoints is gonna create a much richer dataset. Yeah, thank you for that, Michael.


So, 40 years ago, I met this puckish person, this really kind of electrified personality with a beret and wearing a sweater, and his name was Chris Felver, and he introduced himself to me at the Savoy Tivoli, and I was producing what he calls a big-ass reading. I mean, it was a lot of people in that reading, and he said, I'm a photographer and I'd like to photograph the poets. Could you introduce me? And I said, no problem. And suddenly Jack Hirshman popped out of the sidewalk, followed by Lawrence Furlingetti, and four or five hundred other poets. And I guess I had to introduce them all. We did 40 years of work in one evening. The poet exposed, I was so happy to be a part of that, and I'm so happy Chris included me. You know, that idea of frozen in time, you see a photo of yourself. And I saw my photo there from 78, and I said, nearly you've aged well. But of course I have a vested interest in thinking that. Chris, where are you? So there you are, yeah. I hope you'll say a few words. At any rate, this is called, I Want to Be a Dead Poet. It's dedicated to Senol Erdogan, who has translated my book, Elegy for My Beat Generation, copies of which I have in my briefcase, if anyone. It's coming out in Turkish, and such is the world today that he showed up in North Beach at the Cafe Trieste a few months ago. And I thought, my God, a man from Turkey, and his girlfriend is Turkish. She works at a tech firm in Boston. And they sounded like a couple from Marin County. It was really something. I get tricked that way.   We stop for a picnic with Mrs. Satan, who brings fried chicken and potato salad, a cold, cruel wind climbs Tamil Pius as we eat. I want to be a dead poet and learn how to speak the great languages of the world, from Urdu to Bangla, a man of mountain words and desert commas, a brick man and a birch poet. Are the trees violent or violent today? Do you find a distant planet where violins fall like rain? I'm tired of being an old man, feedache, knees weak. They are taking away my license to drive. Rain threatens our beloved town. I'll stand, arms outstretched. I want to be a dead poet. Now it is 5 a.m. I grow frailer by the minute. Yet life is good. I love breathing. Every minute counts. Love is a pain in the ass, but we fall for it. We look like reindeer and our hearts beat like hedgehogs. Oh, to be a dead poet, beloved in eternity. Last week I walked with my partner in the deer park. We fed rabbits and waved to the baboons. I saw two dogs eating sugar from the hand of Gautama. Next week I'll climb the mountain to probe sturdy junipers where snow is like ash, purple lichen, clinches, obdurate rock. I would become sainted and wear a name tag at the literary convention. I want to be a dead poet because nothing else matters. Life depends on such a state. I'll count butterflies in the world to come, eat sounds of drummers until stones are lit like Roman candles.  I need you, your love, your poised brain, skeleton keys, and dust of Oklahoma clinging to my skin.  I hope to meet you on a star when winter dies in my arms and you are witness to a descending goddess, our ancient and inhuman sun rising and sleeping for billions of years, tickling our cemetery dreams. Trees giggle, flowers wear pretty hats, death is proud and primitive. My dogs will wag their tails when I'm dead. Comet, Cosmo, Orion come along, jump into my arms, bring a weapon of leaves scattered on the grass. I want to sing for cosmic rust and ride a comet into the heart of our lonely bed where propped by a pillow I'll be eager to sleep. Thank you.


Hi, how are you doing today? I'm your host, Rich. Here we have a Rich TV Live with our very special guest, John McMullen, the CEO of ScreenPro Security. How are you doing today, John? Doing really well, Rich. Thanks for having me on today. Pleasure to be here. Hey, my pleasure. Super exciting. Can you tell us a little bit about yourself and how you got involved with ScreenPro Security? Sure. I was actually looking for a new opportunity in the world of the public markets. It's something that we could take public. Over a year ago, I started working with the company as a private corporation. And with that, we started doing things such as starting labs, building teams and the such. And when I looked at it, I said to myself, the thesis is this is going to be a real revenue generation company. And that's what we're doing. So we've been building up our revenue run rate ever since last year. And that's why recently we announced a 5 million run rate for the month of February because that's where we are today. And I believe that we're going to continue that run rate for the entire year because we're basing our business on these contracts that we have, which are long-term contracts for film and television for testing and screening for the coronavirus. And that's huge. I have a lot of friends here in Vancouver that are in the film and television business and actually talk to them about what you guys are doing. And they're like, yeah, we're doing screenings like every day, every week. Like it's constant. So it's a great business model. Really like what you guys are doing. Now, screen pro just went public, like we mentioned, just about a week ago now. And right out of the gate, you guys have announced 5 million revenue, which you just touched on for the month of February. Huge revenues for a stock right now sitting at 10 cents. It's unbelievable. Can you tell us how screen pro has built the revenue so fast and what the projections are for 2021? Sure. Let's go back to the beginning. We started with our labs. Our first lab actually in Vancouver was actually started at 800 square feet. The labs are key. These are diagnostic labs that can test for anything. But today they're testing for the coronavirus. And our lab in Vancouver was 800 square feet, started at 500 tests a day. And with screen pros materials, with our PCR machines and everything else that we insert into the lab, that lab today is now 2,800 square feet and growing. And we have the ability to add in more machines into that area, into that lab, to add in more people with our partners and actually expand it. And I will say that a number of other companies in the screening processing world has actually approached that lab wanting to work with them. And they said, no, no, we're working with screen pro. They're our partners, and we are partners with them and we support them. Then we add into what we've done now, because our clients are filming television. And they once they believe in what we could do, we can show them the consistent reporting, the deliveries that actually finding positive test case, delivering them to them, all the information within a 24 hour next day time period, which is critical for a film industry. As you talk to your friends, working on a film set, you have a beginning, a middle and an end. And because they have to screen, they got to make sure that everyone on that set, from the key grips to the food truck people to the actors, to the background actors, that they are safe to work. And actor isn't an actor unless they are working. And that's what we're doing with screen pro, we're getting people to work. So now, those film companies, major ones, that when you're sitting at night watching your streaming services, know that screen pro help get that program to the television for you. Now we have another lab in Ontario, in Guelph, Ontario. And we're mimicking, we can take the carbon copy of what we did in Vancouver, and rebuild it in Ontario, and make a lab that's actually going to be building very quickly. It started day one with 500 tests a week. Now it's 4,000 tests a week, and building rapidly. And we'll be able to modulate that. And actually, and they have materials there, inboxes, ready to fire up when we are asked to do more, we're ready to be there. That's great. Now, obviously, it's a very competitive marketplace right now for COVID-19 testing. What sets screen pro apart from the competitors? I think one thing that is really interesting in talking with other competitors out there, and I have met with some of them, some of them that you've actually had on the show, and I actually applaud their successes today in the market, because this is a global pandemic, but what separates us from them? Hey, we talked about the labs. These are our labs. Other suppliers are actually using third-party labs, such as, you know, there are other ones using life labs. So they're having to get into the queue for a 36 to 72-hour turnaround. Our labs enable us to do 24-hour turnaround. That's a big differentiator for us. The other thing, too, is that we have our own nursing staff. We have actually hired frontline workers, men and women, who are absolutely passionate to work with us. I've been getting feedback, unsolicited feedback, saying, John, thank you for hiring me. Thank you for bringing me on board. This is important, and I want to fight this virus with you. The other thing is that we look at transport. We have our own drivers, all right? And most importantly, we have a secure supply of product, so that when someone needs something, we can get it there. Case in point, we've had days where 800 actors will end up at set for one screening, and all of a sudden, they say, we have 900. We have the materials together to get it done, because it's all about time. When the clock is ticking, we're running, and we're going to make it happen. That's great. You guys also recently announced that screen program approval to bid on the NHS London in the UK. Can you please tell us what this bid is and for how it would impact the company if you were to win the bid? Sure. Okay. There are a number of groups that have been pre-approved. Now, I've been working and known as a personal relationship, the gentleman at Enora Medical. I've known them for many years, and I told them what I was doing. They said, John, we're in. We want to work together. So we have a joint venture with, and we're going to be working together on this. And what they're doing is that they've registered screen pros products with the NHS, and they're moving forward. So once this tender comes out, we will be ready to bid, and I'm leading it in their very capable hands to do it. If they need any information, the needed support, I talk to them two, three times a day every day. We're working very diligently on this. And when the tender comes out and if we win it, even just getting a portion of that will have a monumental impact on our revenue run rate going forward. And it'll go directly to the bottom line. And that's what we want to see. So you're not sitting there excited about your $5 million a month. You're growing that number. And that's exciting for investors to know that, yeah, $5 million a month is great. $60 million a year for a stock saying a $0.10. It's kind of unheard of. You don't see that very often. But this is a company that's not happy with $5 million a month. This is a company that's bidding for more revenue. And that's exciting for investors. It's important for us here at Rich TV Live to understand the team behind Screen Pro. Can you talk a little bit about your roster and some of the key members that are really helping you grow this business? I think we have to look at the advisors of the group. Now, first of all, I have to thank, again, every frontline worker that works with us, our drivers. Our drivers are up this morning in Vancouver at 4 a.m. getting things going. So these are people who are just committed to making this work. My team, my VPs, my advisors, all very, very key people. Everyone has a part to play. When I call my advisors, such as even this morning, I spoke with Jacob Richstein, one of the gentlemen who works with our branding. He doesn't answer the phone, hi, John. He answers the phone. What do you need, buddy? These are the people that I can count on. These are the people that are coming forward and bringing things to me. And there are other operations that are opening up for us with all these other groups. I believe that having an aviation partner, that having doctors, Dr. Gibran Sharif is also on call to answer any questions, because when there's a test, and this is something for the business that gives us our client's confidence, when there is a test that needs to be reviewed, we have a doctor on call, Dr. Gibran, they'll review and speak directly with the patient and speak directly with the medical team at site so that they know that their staff is safe. Everyone play the vital role, everybody. That's great. Now, it's extremely important that we also understand the company's balance sheet. Can you talk a little bit about how much Money Screen Pro has in the bank and if you're planning on doing any dilution in the future? And if so, what would that be used for or any capital raises in the future? Well, right now, well, obviously because of our run rate and because we're new to the market, I will say that we have a number of institutional holders in our company so far. And they've been very open and honest in opening outdoors to us around Bay Street. And I've been having conversations with them and they're very impressed with what they're doing. The Bay Street wants to see us perform. Showing one month of revenue like that, let's get consistent and do it. But the question is, what are we doing with the money? We're having the money going back into the business. We're reinvesting in more materials. We're reinvesting in more people. We're building the business. We're making it real every day. Great. That's great. And can you give us a breakdown of the share structure here at Rich DV Live? We love tight float stocks, companies that are held very responsibly by management. Can you break down the share structure and how much is held by insiders and potentially institutions? If you know the numbers? Absolutely. Well, when we look at this, we have 330 million shares outstanding and 20% is owned by directors and managers. Now, there's upwards of another 20% owned by founders. Now, these founders are people who are bringing new business with me. So they're in there for the long haul. They're like, John, what do you need? So these are the individuals, the men and women who are around me that are working with us. So we have upwards of 40% and very friendly hands. And when you look at institutional investors, they said to me, quite upfront, we're going to hold this stock. And if you need us, call us. Wow. Okay, great. And do you know what percent is being held by institutions? Institutions, I can actually give a rundown and I'd be more than happy to share than another one, because you know what? I'll get permission from them first. Okay. Where I expose their position. I think it's important for them to feel confident and know that their business is their business and they have my support and they have mine. Yeah, that's totally fair. What's the main goal for ScreenPro in 2021 that you want shareholders to know about today before you leave this interview? Well, again, I look forward to our next conversation. And I also will say that for 2021, what we're building on here is a model that is sustainable. We've got a run rate of 16 million a year. Now, I've talked about business outside of the country, but we also have to look at the business that's expanding within this country. We're getting more opportunities, more referrals, more business. As I said, we just had a feature film being delivered to us by one of our main clients. And I can tell you that Vancouver, as I'm sure is your residence, you'll see a lot more productions coming up to the province because of the Canadian U.S. dollar comparables. It's actually more effective for film and television companies to come to Vancouver, this great province and also across Canada. And by the way, film companies are already asking us to open up operations in Calgary. And so, reviewing that opportunity right now to see if it's viable. Because right now, we also have, just so you know, we have productions that are in Montreal. And just for everyone's, you know, do you have a top line one more time? We've done 23 productions. They're current. We're working. We have 23 locations running. And that's in Montreal, that's in Ontario, sorry, Quebec, Ontario, Calgary, Vancouver. And we're able to ship and move our tests to our labs to get it turned around to one of our bases. That's a compelling argument for people to sign on with us. So I believe we're going to further grow within the domestic business. And working with my team of advisors, we're able to expand the global business. So that's what we're working with at Springbrook. Here at Rich TV Live, we've got investors in over 100 countries that will be watching this interview. What is the best way for them to get in contact with you? Well, they can go to the website, but they can always email me. My name, my contact information is on the press releases. I'm already taking calls from your, from your viewers right now. And I'd like to say a shout out to all of them and thank you for your questions and reaching out to me. I'm open to discuss things with them. And I'm open to let people understand what they have, because I believe shareholders have a right to know what they own. And I'm more than happy to sit down and talk to them. So if any of your investors want to do that, they can send me an email. I'll get back as soon as I can. And then we can talk with, and we're also bringing on people who can answer those questions online. So we're working on that, but you know what? For today, let me be the face of the company and let me talk to them directly because if you can't talk to the CEO, what are you talking about? I agree 100%. Thank you so much for your time today, John McMullen, the CEO of ScreenPro Security. Now, everyone that's watching, please do your due diligence, do your research and consult a financial advisor before you invest in anything that we talk about here at Rich TV Live. Do your due diligence and make sure if you like the video, smash the like button, comment on the video, subscribe, hit that bell for notifications. So every single time we go live or we have a video or we have breaking news, you get access to it first. John McMullen, the CEO of ScreenPro Security. Thank you for joining us today, John. Thank you very much. And let's just say, as I said, all our clients test negative, stay positive, go with ScreenPro. I love that. Thank you for joining us today. I wish you all the best of luck on your future endeavors. Myself and our entire community will be watching very, very closely. Once again, a company doing 5 million a month, 60 million a year and growing, currently sitting at 10 cents. These are the types of companies we love to position ourselves, companies that are revenue generating, companies that have a strong management team, growing revenue, going in the right direction with a product that is a need, not a want, especially in a COVID-19 world right now in 2021. I think the upside is tremendous. Thank you for your time. If you guys are not winning, you're not watching. This is Rich from Rich to Be Live, and I'm out. Have a nice day, everybody.


Hi, I'm Kashyam, the head of the United States, and I saw you previously in Voice of America, Pergen. Yes. Nice to meet you. I voted in first time in presidential election. It was first time that I voted. I woke up previous election. Are you happy? It was for me that who won the election. And how old are you? What do you do? I'm old. So what do you think now of the new president-elect, Rouhani? I think Rouhani is an experienced man and can help to solve Iran's problems. I'm optimistic about the problem, but I'm not sure he can decrease Iran's sanctions because the Christian pressure on Iran is high, and economic problems is high also.


The latest advances in virtual reality, VR, have brought about a new era of locomotion experiences. Researchers and users alike are interested in understanding and experiencing the latest VR locomotion techniques. This paper examines the existing literature on VR locomotion from 2014 to 2017, identifying 36 relevant papers and categorizing them into seven different types of VR locomotion. These include motion-based, room-scale-based, controller-based, teleportation-based, and more. The study concludes with a discussion of the implications of the findings, suggesting that future research should focus on user experience and interaction design. This article was authored by Costas Belletsis.


This 10th year of Daily Tech News show is made possible by you, the listener, thanks to every single one of you, including Dustin Campbell, Dim Deputy, and Brandon Brooks. Coming up on DTNS, what the heck is Blue Sky and why are all the cool kids talking about it? Plus, an open standard to stop misuse of Bluetooth trackers, and Nika Monford tells us about a leader in the effort to represent black people in the field of artificial intelligence. This is the Daily Tech News for Tuesday, May 2nd, 2023 in Los Angeles. I'm Tom Merritt. And from Studio Redwood, I'm Sarah Lane. From the Atlanta area, I'm Nika Monford. And I'm the show's producer, Roger Chen. Oh, my friends, we've got a lot of practical news. And if you've been hearing all the folks talking about Blue Sky, we've got that news as well. We're all on it, so we're going to explain it to you. Let's start, though, with the quick hits. Apple issued its first rapid security response update available for iOS 16.4.1 and macOS 13.3.1. Apple introduced rapid security response in iOS 16 and macOS Ventura. It moves some system files that are likely to need more frequent updates, particularly WebKit related files, into their own secure volume, which then reduces the size of patches and the time it takes to install those patches. In some cases, they can be installed without a reboot. The first public update does require a reboot, but it was smaller and faster to implement. No word on the specific flaws addressed in the update. Some people, though, experienced an error when it first rolled out. Apple has now fixed that issue. Yeah, mine went painlessly when I did it this morning. Same thing. As of May 2nd, Utah requires sites that publish or distribute material deemed harmful to minors to perform age verification for all users. The definition of harmful material is very extensive in the law, if you want to take a look at it, but mostly it applies to adult porn content. In response to the law, pornhub.com has started serving a video message on its website explaining its opposition to the Utah law and asking visitors to contact their representative.  The rest of the site is unavailable. Pornhub already verifies ages in Louisiana in partnership with a third-party service called Allpass Trust, so they're not protesting it there. It appears that they believe the Utah law is not well written and lacks proper enforcement and provisions for device-based verification. Motorola announced its new Edge Plus flagship phone is coming to the US starting at $799. On top of typical flagship specs, it offers a 165 Hertz, 1080p OLED screen, 68-watt wired charging, and IP68 rated weather resistance. The phone will be available unlocked on Amazon and also Best Buy on May 25th, and later through carriers Boost Infinite and Consumer Cellular. Yeah, it's a nice-looking flagship phone. IBM CEO Arvin Krishna said the company will suspend and slow hiring for some back-office jobs, including some things done in human resources. Krishna told Bloomberg, and this is the important part because I've seen every headline misrepresent this, Krishna said, I could easily see 30% of that getting replaced by AI and automation over a five-year period. Now, Krishna said things like workforce composition and productivity evaluation would not be automated. They would automate mundane things like employment verification letters, other paperwork. Basically, IBM is cutting back on jobs or at least on hiring in advance of an expected recession and also planning for future efficiencies. Back-office roles amount to 26,000 jobs at IBM. If you take 30%, that would be 7,800, which is the number you keep seeing in headlines. But no, Krishna did not say IBM would replace 7,800 jobs with AI. Yes, there is the potential for that to happen, given what Krishna did say. E-Scooter maker Unagi announced its latest version, the Model 1 Voyager. You can buy it for $1,190 or rent it for $89 per month, which includes maintenance and also insurance. It uses the same design as the previous Model 1, but double the range at 25 miles and a system to give you an estimate of how many miles you have left on that battery. That is, look at the quick hits. All right, let's talk a little bit about this tracking standard. Multiple companies have joined together to propose an industry standard to the Internet Engineering Task Force, aka the IETF, for unauthorized tracking alerts. So it's a standard for when to provide these alerts, how to detect unauthorized trackers, et cetera. Basically, warning people if a Bluetooth tracker like an Apple AirTag or a Tile Tracker is on their person or in their car or something without their knowledge. The group proposing the standard includes Apple, Google, Tile, Samsung, Chipolo, Anchor's Ufie, and Pebblebee. And if you can think of a tracker that's not in there, let us know, feedbackanddailytechnewshow.com because that covers a large majority of them. Features are expected to be complete by the end of the year and once they are complete in the standard, implemented in both iOS and Android. Meaning you won't have to have a separate app in Android to be able to check for Apple AirTags. That will mean that Apple AirTag alerts will work across those operating systems and all other tracker makers like Tile will be able to join the standard and work with those operating system tracking systems as well. Nika, what do you think of this industry effort? I think it's probably the best thing to do when AirTags were first announced. Everyone was excited about them. And then of course, negative threat actors if you want to call them started using AirTags for things that AirTags were never meant to be used for. And so people who had non-Apple phones didn't have a way to identify if an AirTag was on them or unknowingly or if they were in their bag on their car. And then they had to be a whole separate app and there was a whole lot of talk about that. Is Apple doing enough to protect people, particularly women were the largest people who were being targeted for this, for stalking, harassment, you know, those types of things. So I think this joint effort from most of, like you said, the app, the, you know, tracking type devices coming together. I think this should hopefully quell some of the discourse and also make people safer to use whatever tracking system they like with a certain level of confidence that they'll be safe. Yeah, I have a fun AirTag story. My dog was staying with a friend of mine a few months ago and the friend wanted to make sure my dog was safe, put a little AirTag on my dog's collar and then everyone forgot about it because then the dog was back in my possession, it was safe and sound. But at one point, because I hadn't really thought about the AirTag, I got a little alert in my phone like, just so you know, there's an AirTag that's, you know, with you. Oh, because it wasn't yours. It wasn't mine, exactly. And it was totally above board. Like nothing weird was happening. But if it was something weird that was happening, it was probably gonna take me a while to notice that AirTag if I hadn't gotten that notification. However, I use iOS. So if I had, I don't know, a variety of, you know, other mobile devices that could also ping me and be like, heads up, just so you know. I think that's better for everybody. Yeah, the important part about this is that you don't have to be using a tracker and you don't have to think of all the possible trackers someone you would use against you. Once the system is in place, your operating system is either Android or iOS, it's gonna let you know. You don't have to do anything. And that's, I think that's the key here. Absolutely. Now that we've solved that. Really? Yeah, I'm kind of, I was thinking about the story earlier of like, okay, well, I think it's, I think, you know, getting more updates on, you know, if a tracker wants to let you know that it is near you. So your whereabouts are being possibly seen by one or more people, all very good. I think that, I don't know, in the same way that smart home devices, we're getting closer to them all talking better together. This is sort of the same idea, but very much more in a safety situation. Especially because in a home situation, you're the one deciding to use the devices. In this situation, it's the nefarious person who's deciding to use the device. And so they're shifting the responsibility off of you to protect yourself to just be built into the operating system. I think that's good. Matter for trackers. Yeah, yeah. Good way to put it. All right, Nika, it's time for another edition of Teching While Black, where you shine a light on a technology leader in the black community that we might not have heard of yet, but we'll be glad that you told us about. So Nika, who are we highlighting today? Today, we are highlighting, right yet, a bi-bay. She is the co-founder of Black in AI, which is an organization that is working on increasing the presence of black people in the field of artificial intelligence. Throughout this show today, we've talked about AI a couple of different times. So it's one of those things where we're making sure to... I thought it was important to highlight someone who is a minority, who is in this space, and who is working to further encourage other people of color to be in this space because as we've seen already, like I said throughout this show, the way that AI is really taking over, it's the new hot thing in the tech space right now. Additionally, into co-founding this organization, she is an assistant professor of computer science at UC Berkeley. She happens to be the first black female professor in the history of the department and the second in the history of the College of Engineering.  Additionally, she is the first black woman to complete a PhD in computer science at Cornell. So lots of firsts here for her. There's a slew of other awards and honors that is honestly too much to go through on this show right now, but I definitely encourage you to go and check out her work, the work that she's doing in AI and the way that she is facilitating the inclusion of black people and other people of color in this space because as we know, when everybody is included, especially in new technology, everyone is the better for it. One of the big problems you hear about with training sets is that they aren't comprehensive in representing the world, right? That they might skew in one particular way and so having a diverse set of people working in AI kind of helps correct for that. You've got somebody in there that's like, hey, wait a minute, this data set doesn't represent me or my family or people who look like me and so having somebody working to make sure that you include a diverse set of people working on AI is a good way to prevent that bias from creeping in or at least reduce it. Absolutely, and a prime example of that is recently in the field of art, there was an image of a child who drew a hand and it had like flowers sprouting out from it and it was a brown hand and they used generative AI to create a realistic photo of the drawing. I mean, it's obvious when you look at the source, it's the child's image because you can see the crayon, streaks and everything, but when they created the AI image from the source image from the child, the hand was white and female and not black and nondescript because it was just like a regular if you kind of trace the outline of a hand type of hand but the way that it was able to take that and convert a very obviously brown hand into a white hand but the integrity of the image was still intact, the flowers coming out of the fingers, you could definitely tell that that's where that image was sourced, so things like that because it's all based on the data that is trained on. So if it doesn't have enough data to train on, it doesn't have a way to identify that, oh, this is a brown hand. So what I create in the AI generative process should be a brown hand as well. Well, last month, the Economist Korea reported that employees of Samsung had uploaded sensitive information to open AI's chat GPT on at least three occasions. The first occasion, it was asked to check database source code for errors. The second occasion asked for code optimization and the third uploaded a meeting recording and asked chat GPT to generate minutes. Okay, in all cases, the data was confidential though and chat GPT uses data prompts to train its models unless you opt out. Now at the time of the incidents, the only way to get rid of chat prompts once they're made was to delete the entire user account. Initially, Samsung responded by limiting the length of the prompts, its employees could enter. Now, Bloomberg has seen a memo indicating that Samsung has banned employees from using generative AI tools, including chat GPT, Bing Chatbot, and Google Bard on company-owned devices and any device using the company network. The measure however is temporary until Samsung can build better security around chatbot use. Open AI is slowly coming along and adding some additional ways to control the data you add to its prompts. Tom, can you tell us a little bit about those products? Yeah, yeah, they're not terribly user friendly. To get your data removed without deleting your account, you can try filling out the open AI personal data removal request.  Also, if you're not in Europe, they might not honor your request. This is really done in response to the lawsuit that was brought in Italy. If you want to stop collection before you enter prompts, if you want to opt out of that data collection, any user can fill out the user content opt out request. It's a Google form and that will prevent your user data from being used to train open AI's models. They give you lots of warnings about how this will reduce the effectiveness of your results. And you can also go into chat GPT's account settings and disable training on your data, though that method will disable your chat history as well. So these are not terribly user friendly ways to protect your data because open AI wants to use that data in the training set. What I thought was kind of the first thing that stood out to me about this was, you hear so much about, well, AI is going to take humans' jobs away and then you've got Samsung employees being like, no, no, it's helping us. This is good stuff. Let's check some source code for errors. Maybe there are some, like optimize some code. Maybe that really boring meeting that we all had to sit through for two hours earlier today, we can generate some minutes from that. But yeah, if you're Samsung, you're like, no, no, no, no. Let's not do that until we can assure that confidential information isn't going into, you know, the larger, I don't know, AI pie in the sky and end up coming back to bite Samsung in the, you know what, at some point, because that's what the company would be afraid of. And honestly, it's smart. I think there are not a lot of guardrails on the different uses for AI. And I think this is where we can get into a bit of trouble is when we don't have guardrails to protect companies, individuals from, you know, their data being used incorrectly or without credit or in this case with proprietary and, you know, information that they don't want out there being susceptible.  But playing a little bit of devil's advocate, is that really going to stop folks from using it? Because if you're not on the company network and if you're not on the company device, you can still use these tools. There are ways around it, but I think they're kind of slowing, you know, putting some hurdles in the way of people doing it. I always think that the same thing when I hear on company devices, it's like, well, okay. So the company says, all right, wasn't us. We did what we had to do, but yes, your employees have other options. Yeah. Like you said, it takes a little extra work, but if it's going to save you a little extra work, maybe it's worth it. I think the solution here is either Samsung figuring out a way to safeguard this stuff. Because honestly, once it gets trained, it's not like the data is in the model. I think a lot of people have that misimpression. The risk isn't the training. The risk is it's sitting unprotected in the data set and somebody gets access to the data set and then finds that stuff in there. So you don't want it to go into an unprotected data set. And I know OpenAI is working on like versions of itself that you can buy and locate in your own company so that you're training it and you're protecting it and it doesn't go into the cloud. It doesn't go anywhere else. Feels like Samsung needs something like that. Yep. Well, folks, if you want to join in the conversation and you've got some ideas on how Samsung could fix this, fix this or OpenAI could fix it, get in our Discord. You can join it by linking to a Patreon account. Just become a patron, patreon.com slash DTNS. Many people are talking or looking or talking and looking for an alternative micro blogging service Mastodon, one popular option and it's rolling out new features to make it more appealing. Some folks said servers, we don't really understand. It's kind of hard to figure out how to join Mastodon. Mastodon says now, when a new user signs up, they'll see two server options. Mastodon.social, which is already a very popular one or pick my own server, which you can already do. Mastodon is also adding things like quote posts, improve search and groups to make it more like a micro blogging service that you might already be familiar with. But the Darlene of the Technoradi last week became Project Blue Sky. So Nika, let's remind folks what Blue Sky is and how it differs from other Twitter alternatives. So Blue Sky started, was started by Jack Dorsey, we all know, was the CEO of Twitter as a project to create a decentralized micro blogging system. It was spun out at founding. So no, it's not currently a part of Twitter now. Now, Mastodon is also a decentralized service, but Mastodon is built on an activity pub standard. Blue Sky chose to make its own open standard called AT Protocol. Among other things, the AT Protocol has more robust account portability. Portability compatibility that activity pub with the aim to make it easy to move between servers without losing posts, followers or follows. So tell us a few things to know about Blue Sky, Tom. Yeah, thanks, Nika. Here are a few things to know about Blue Sky.  It's gonna look very familiar if you get in. It only has one server right now that is run by the Blue Sky project. So you don't have to choose. And also it's not really decentralized because there's just the one server at the moment. They do plan to open that up and interoperate with other people on the AT Protocol. You have to get an invite to join right now. You can't just join in. So yeah, 300,000 people or so have downloaded the Blue Sky app, but there aren't that many users of Blue Sky because a lot of people downloaded it just to get on the wait list. A few things about how it works. It has a 300 character limit. You can like, reply and repost. There's a following feed and a what's hot feed. Following is people you follow. What's hot is just kind of the fire hose. It is algorithmically driven though. There's a mobile app, a website at staging.besky.app and a few third party apps being developed for it. Users receive one invite code every two weeks that they're on the platform, though the Blue Sky devs have been going around giving out extra invites to folks they see using the platform well. Now, we've all been lucky enough to have somebody invite us to Blue Sky.  So first, let's see if I can get through this without portability, compatibility. I mean, but so far I've enjoyed it. I think because it is so new and there aren't as many people on it. To me, it gives me a lightness of social media because it's not as, you know, like you said, it's just not a dumpster fire. Yeah, it's not crowded. It's not crowded.  Some of them are on Blue Sky as well. And so it's like a streamlined version, again, because this is an invite-only beta that makes sense that that's the way it is. But for an initial offering for a new platform, I think they are, from what I can tell in my usage, I think they are going in the right direction and I expect tons of improvements to come because there are, as you said, limitations. But I think where they are and where it seems to be going is so far pretty good. And I have to say on the portability capability part of it, being able to use my own domain as my username and the process to get that to happen on the app was pretty seamless. I was pretty surprised how easy it was. Now, I was, when Macedon first kind of came out as a potential alternative, I went over and I was like, I'll try Macedon and then I got over there. And I'm a tech person. So I know kind of how to maneuver my way around. I was like, this is a whole lot to get this set up. And I was like, I'm going to not do this and I'm just going to go back and suffer through Twitter the way it is. So for Blue Sky to have that ease about it was a big winner for me. Yeah, I kind of feel the same way you do, Nika. Macedon, which I'm on, it was not hard for me to understand how it worked. And Annaly Nuits was on the show recently and made this great comparison. Macedon versus email. When you have an email address, nobody talks all that much about what your email address is. You just use the email protocol. And you share information and we're all very used to that. You might use IMAP, you might use Pop3, right? But nobody cares what you're using. Exactly, that's behind the scenes. And maybe there's some people out there. Well, I know there are some people out there still paying for AOL kind of thing. But it took a while for some of the normies to get on board. But we're all used to that and it's not a conversation to be had anymore. And I think that you could see Macedon that way. What I found interesting about Blue Sky was once I realized that there was a desktop client, I was much happier. Not a desktop client, but a web client.  But I was like, oh, this is Twitter. It looks exactly like Twitter. OK, the fundamentals are supposed to be different than Twitter. It's supposed to be based on a more open standard. But it looks and acts like Twitter. For example, the design is Twitter. The design, I mean, it's Twitter. The user experience is definitely Twitter. Yeah, I mean, it's not hard. If you're like, I liked Twitter and I want something like Twitter, but not Twitter, Blue Sky is for you. For example, I looked at the What's Hot feed last night. It was all Met Gala photos. Jared Leto in a CAD costume. I'm like, this is my stuff. I'm following people left and right.  And I'm not saying that Twitter is just a bunch of nonsense because it can be, but it can also be an incredibly important news source, which I think for our purposes it probably is for this panel. And Masternon is that almost plus, but you still have this barrier to entry for people who go in servers and whatever. So I think Masternon is doing the right thing here. Trying to just be like, let's simplify this whole thing. Let's stop giving you all these server options because it's confusing people and they think they have to run their own servers type thing. But Blue Sky, I don't know. It seems like it seems like the one to watch. It is very clicky right now on the one hand. And what do you mean by that clicky? Well, on the one hand, it's low population. So I feel like, oh, my voice can be heard and I can have a conversation without people piling in like Nika was saying. But also it's a lot of people talking about how cool it is to be in Blue Sky and let's make sure we make it in our image before the randoms get here. Yeah, right. So that's the clicky part of it. Which is in Twitter was the exact same way. Yeah, right. But naturally, not exclusively, right? You didn't need an invite to get on Twitter in the early days. Right, right, right. Yeah. I know that there's some talk about Blue Sky's instance of this staying invite only to control the growth and then opening it up to third parties and saying those folks can decide whether they want to do invite system or not. When they launched in March, they said that they would only do the invites until they got their moderation system in order. So I'm not sure where they're going with that. But I kind of like the idea that they might keep it invite only to control the growth and encourage people to go to other servers once they're interoperating with other servers. I think that would be a good way to get other servers to interoperate. I know originally I think they thought Twitter might adopt the protocol. It does not look like that's going to happen because Twitter cut off their funding last year. They used to get money from Twitter to be developed when Dorsey was in charge under Musk. They are not getting that money anymore. So that would have been a great fuel for their growth. I don't know if that's going to happen anymore though. Yeah, it's really interesting and especially on kind of like the clicky and it kind of made me think a little bit about the way Clubhouse was when it first came out. Yeah, it was, you know, that type of experience. But I have to say for me, for the people that I've seen, I don't get the sense that oh, it's like we're trying to only the cool kids come in to me. I haven't had that experience. I know some people have seen that and have had that experience. It hasn't been that way for me. But I know there's another coming upcoming that's pretty popular social media site spoutable that is out and a lot of people are using it very. You can put over your legacy Twitter check over there. They have a tool that does that and quite a few notable people have signed up and are using it. But there has been a little bit of controversy on office on the Twitter timeline of with the founder what seems to be some people saying kind of taking shots at Blue Sky and like some back and forth. So it's I think competition is good for the consumer. So it means that there are different platforms out there that are really trying to create a product that is going to be better for the consumer. So I'm all for it because at the end of the day you have to at this point you have to try different things to find out what platform is going to work for you. And right now it seems like we have some pretty good options out there. I would just encourage the devs at Blue Sky to go invite a few less like-minded people. They seem to be a lot of like-minded people inviting like-minded people and I think I think you could you could use a little more of a mixture there. It's like the bouncer at the club. Come on, you know, not just your friends. Everybody. Nika Monford is one of our friends. Always nice to have you on the show. Nika, let folks know we can keep up with your work. For the most part you can find me on all the social media outlets at tech savvy diva. You can also check out my own podcast is a weekly podcast that I co-host with Terence Gaines which is called The Snobbo West Show where we talk about all things Apple and then some other tech as well. So definitely check us out. We our shows come out on Fridays unless you're a Patreon supporter and you get the show on Wednesday live with us. How about that? Good to be a patron. Also a special thanks to Chad Johnson, one of our top lifetime supporters for DTNS. Thank you, Chad. Thank you for all the years of support. Yay, thank you, Chad. Chad is one of our patrons and our patrons can stick around for the extended show Good Day Internet. The writer's guild is on strike and it's streaming TV's fault. Ah, it was DVDs last time. So here we are. We're going to talk about that and more on Good Day Internet patrons or people who are just becoming a patron right now. Stick around. You can catch our show at DTNS is live Monday through Friday at 4 p.m. Eastern. That's 20 hundred UTC and you can find out more at daily tech news show dot com slash live tomorrow's Wednesday and you know what that means. Scott Johnson is with us. We'll talk to you then.