Back in the old days, if you didn't have a radio station or a television station or a newspaper of your own, you weren't able to communicate. Culture is about participation. It's about being together and exploring how we can make our society better. The world has changed. Citizens are no longer just consumers of information. So with this sort of crowdsourcing, what we call citizen science, we're really seeing how communication technology can transform science. Now our understanding is limited not by what data we can get about the universe, but by how many people we can put in touch with that data. We designed the Sensing Umbrella to be a node for gathering, displaying and sharing hyper-local air pollution data. Each umbrella collects nitrogen dioxide and carbon monoxide pollution levels in real time, displays it above the user's head and pushes the data to the cloud. Technology is a really important enabler so that we can communicate in really remote areas about this issue of climate change. And that's something that hasn't been possible just even 10 years ago. Rizomatica is an organization that I helped start. Basically we help people get set up and running with cellular service in their communities where no other coverage exists. In places where it's not profitable for a large operator to go, people can do it themselves. Because it's really difficult to transfer from the field to the community from one hour to two hours. We work in the field, sometimes there are virus bites and probably anyone can take a cell phone and they can communicate quickly. What's happening now is just kind of a shockwave. What people can do. What would we say now? It's like I'm going to lose my arm. Because over there, over there comes the source, let's say, the income that I can have. We are making a technology to make sure that people who have lost their hands are able to take their hands back again. We used 3D printers to make things, but we are trying to make it more affordable and wider. What is done by partners like IMTFI is that poor people actually have very complex financial lives. Through the use of technology, it will simplify the learning process, the teaching process. This program benefits students. Mathematics has been a disease, most of us hate it, but this thing makes us like it. This is a huge step forward for the developing countries of East Africa to have a high capacity fibre optic cable which will probably see them through the next 20 years and really bring them into the 21st century. Technology is one thing. We can do the best technology possible. But what is even better is people who use that technology to do their jobs wonderfully.


Biggest, I'd say flaws with how people approach their pain from the workplace. One is they'll have these ergonomic experts that will come in that will adjust their chair and their desk. And they're basically trying to band aid all of the pain issues, not really understanding that's not the root of the pain. It's because they had lack of mobility and they're weak. And then the second issue, if you talk to the average person about their pain, they'll say, well, I need to be more flexible because I'm tight. Flexibility is not mobility. Flexibility increases your range of motion or is your range of motion? Yeah, temporarily. Mobility is, are you strong and controlled within that range of motion? You know, I like to use the example of my one-year-old son. He's got incredible flexibility. I can put him on the ground. I could take his feet and put him by his head and put him in the splits. But he's got no mobility because he has no strength. So I think people confuse that. They think it's my desk, it's my chair, or I'm tight. No, tightness is part of it.  That's what gives you the pain for your mobility.


Hello. My name is Matthew Lincoln. I'm the Collections Information Architect at Carnegie Mellon University, and I'm joined today by my colleagues Julia Coren, our University Archivist, and Emily Davis, also an archivist with the University Libraries. We're going to be talking about a project centering the human expert experiments in computer vision infrastructure for digital collections management. What's not with us today is one of our colleagues on this project Scott Weingart. I also want to call out his important contributions to this project as a project manager. So first to give you an overview of our presentation. I'm going to be handing it over to Emily Davis to talk about CMU's General Photograph Collection and the particular challenges it presented that were the impetus for this project. We'll cover what the goals of this prototype project were over the a couple months that we did it. Then I'm going to walk through the results of the specific computer vision tasks that we did. And then finally we'll return back to looking at takeaways for archival management takeaways for software design and implications for future computer vision research and everything that we'll be talking about in this presentation. So here's a white paper of this report. You'll see the DOI at the bottom of the screen, where you can read in much more detail about all the things that we're talking about. So without further ado, I'll hand it over to Emily. So the General Photograph Collection consists of roughly a million photographs, million images, and they document nearly every aspect of campus life since the university was founded in 1900 to present day. The vast majority of the collection consists of photographic prints, 35 millimeter negatives, as well as born digital materials. And most of the photographs were taken by the university's marketing department. And we still get transfers of new images on a fairly regular basis from them. To date, we've digitized a little over 20,000 images, and most of the digitization has been driven by requests from researchers on various departments around campus, alumni, their family and exhibitions that we do here in the libraries. And unfortunately, currently, all of these images are not publicly searchable via our digital collections platform, and that's mostly because of compatibility issues. But for the past year or so, we have been working to migrate to a new system, and we hope to have some item level metadata to make these photographs discoverable. But as we probably all know, item level description at scale is hard. And so the collection right now it's currently organized at the item level so or at the level of like a single rule of film or a CD. These containers have fairly minimal description, and it doesn't go down to the frame level. And since the description is minimal and the contents of a single rule can be diverse, we don't want the images to inherit the existing metadata because then every image in a roll of 35 frames would have the same description. I'll give you an example. If we think of, we have lots of roles just described as commencement. And if you can imagine the activities that happened during a commencement ceremony are pretty, you know, buried anywhere from the president giving an address to people receiving honorary degrees into the commencement address itself from a visitor so it'd be better if we could describe all that stuff and more detail would be better for our researchers better for us to find things down the road. Additionally, many of the images in the collection are close matches and near duplicates as you can sort of see here. So if we were to migrate all of these similar images to our new system, we would really clutter the discovery interface, and we really don't want to make users way through eight or nine images that look, you know, pretty similar. Plus, if we were to migrate all these close matches, it would really increase the amount of item level metadata that we would need to create. So it was with these challenges in mind that we were excited to partner with Scott and Matt on the campy project to explore some solutions. Yeah, so the campy project standing for computer aided metadata for photo archives initiative. The project that we did over the summer of 2020. The concept for the project was a prototype implementation of a system and user interface. Again, this was a two and a half three month long project we were not building a production system. Our goal was to try out some software and come up with a deliverable that is high level requirements for what a production system would look like in the future. This is a formal report that discusses the specific effectiveness of methods of methods for similarity search methods for duplicate detection image tagging at scale. And then also something that we tried is doing object detection using commercial API's. So we got the question sometimes, why, you know, were we not trying to train a computer vision model to actually do this work to actually make the decisions about what photograph get tag gets tagged as what. There are a couple reasons for this first of all custom computer vision training for individual projects where you have known training data and known set of tags that you want to match things to. And then you train a model for that. This is a tricky fit with real life archival workflows. This collection in particular gets continuous new accruals every year. There's new digitization. And then there are expanding descriptive needs, the dictionary of terms that archivists might want to be able to add to these photos continually expands, which doesn't really fit with that sort of one off project base train the model and it's good to go forever. In many cases, the visual properties of these photos alone aren't usually sufficient to usefully describe them completely. And the computer only gets access to the pixels that it sees what we were looking for was a system so that archivists could work hand in hand with these tools, so that they can bring archival context, alongside the things that the computer does well visual search similarity and connection and combining that together to help prioritize photos for description, keeping the archivists in the loop rather than trying to outsource all the work completely to a machine. So, it's, we didn't train an entire new model instead we used what we call an off the shelf model a pre trained model. And for example we used inception v3 and also experimented with ResNet 18. It would not work very well if we wanted to use them to apply our own tags to these pictures, but they're good enough to give us pretty useful visual similarity search. The example you see here is a picture starting of our College of Fine Arts. The photograph here was an input to the query and then all the other photographs are from all across the collection, different years of photographs different roles of film that have been digitized, returned by this very generic visual similarity search. And so, even though these off the shelf models aren't perfect, they can be good enough for visual search they can be good enough when a human is in the loop. So, one of our first major tasks was this close match detection the problem that Emily had described of having a lot of these very similar photos all taken during the same shoot. Using the similarities detected by inception, and these other computer vision models, we could use an algorithm like DB scan to help cluster together potential close matches, and then using these again potential suggested by the computer. We built out user interface that could help the archivist prioritize quick review easy examination of details you can see here clicking into look at details is this actually the same set of students at the Carnegie Mellon's anal buggy race, or is this a separate set of runners. And during this decision making we also built a back end to then capture all of the editorial decisions that were being made. And doing this kind of close match detection was incredibly useful we found out almost a third of all the photographs that we had were in fact duplicated or very close match photos. Most of these were photos where we had to three photos that were very similar, but we had several sets of photographs, especially from things like faculty portrait shoots, where we would have dozens of photographs that were all very very close together, but we would want to be able to add on the same metadata and we want to be able to group them in a search and discovery interface. So this was our first step finding these close matches. Our next step was then to figure out how do we turn this into similarity a tagging. We wanted to be able to leverage our existing organization that organization at the level of a role of photographs. What we were do was build an interface for an archivist to select a photograph they knew was of commencement, which would then bring up similar photographs from across the collections. Starting from that basic visual search, our UI would then allow the archivist to go back to that original context you see in the slide out drawer on the right hand side, which will allow them to quickly look through the entire collection and in this case sorting out which photographs are showing student faculty processions versus which photographs are showing other parts of the event. Having tagged an entire collection, the archivist could then go back out to the similarity search restart the process. Pull up another similar photograph perhaps from a very different part of the collection. And then from there, begin the process again. This allowed again us to put together the similarity search to begin the process to help that initial prioritization, but then providing that original archival context, so that the human could make much more informed decisions within the context of where these photos were first taken using this process in a matter of just a few days, we were able to apply tags to almost 8000 photographs from this collection. And when we looked back and did an analysis to try and compare what kind of metadata were we able to add to these photos that wasn't already inside that higher level of organization description so descriptions that were on the levels of those roles of negatives. We found that we often had a substantial addition to that already existing metadata. And so by adding those tags, we were enhancing a great deal of metadata, above and beyond what we already had from that higher level description. Of course, if you look down below, you can see certain tags like football or robot would already be well represented. These collection titles, and so this was an interesting point for us to reflect as well on what were perhaps biases in the original description of this collection. That would happen by the photographer who was taking the photograph at the time, compared to how today, we might want to be able to tag these photos for access and discovery. The final task that we tried out was experimenting with a commercial provider Google's cloud vision API to try and see how well it's tagging entire photographs, or identification of objects within photographs could be at doing some of this work. On the one side you see a table showing the top 10 tags that we got. This is probably not much of a surprise but highly generic image description API is like Google cloud, provide labels that are way too generic for our particular context.  This we know these are applicable tags and they're not going to help us sort out this collection. In a more detail in our report, some of these topical labels such as students teaching laboratory or theater did help identify some photos for consideration. But there is very poor recall, right so there were an awful lot of photographs of students that Google would not recognize as students. So we would suggest that this kind of public API, although very easy to use is not providing very useful data for public consumption. There was a full amount of review and would miss an enormous number of photos. That said, it could be useful as an internal search tool sort of first pass of adding some metadata that could help archivists and metadata specialists sort through the collection find photos that they weren't finding already, and then doing proper tagging on them. These are the results from the Google cloud vision API trying to tag the entire photograph. We found a little bit more success with object localization so trying to find what part of the photograph is showing something here you see all the neckties that it could find across our collection. We found that this was actually a much more discriminating. And we thought could make possibly for very engaging entry point into the collection. So while this is services like Google cloud vision API aren't a cure all for the kinds of metadata concerns we have. They are worth exploring, as long as users are aware of their limitations in this particular context. So let's sit back over to our archivist to discuss their reactions and the implications that this initiative had for photo archives management. Thanks Matt. For the archives part we were very pleased with the outcomes of this project. We need to start by classifying that with the elephant in the room which is that we did not have a dams for our photo collection. So prior to this project so accessing and identifying photos in response to reference requests or media reuse has been very difficult and very laborious. So just being able to pull up photos quickly, see them side by side was hugely beneficial for us. We also some larger implications for how this type of model could be integrated into archival archival work moving forward. First and foremost is the duplication of nearly identical photos. As archivists know the photo collections we are receiving are growing exponentially in size as our marketing departments and photographers have adopted born digital photography. Where in the past we might have gotten 36 images we now may get 360. And most of those images are highly repetitive. Some archivists have proposed using a type of random sampling to reduce the size of these collections. But Campy has demonstrated that there are ways to do that in a more knowledgeable and mediated ways where rather than randomly selecting two images from a portrait shoot, we can quickly find the best image and remove the rest from our collections. The visual search was also very useful for access and reference. It allowed us to identify new photos for example during the course of this project we were asked to identify the earliest photo of a computer lab on campus. I was able to locate a computer lab photo from about 10 years after it opened and then using visual similarity search was able to go back and find an untagged earlier photo of that same space. We also think that as Matt mentioned there are some potential novel applications for search using things like ties to give people new entry points to our collection. One kind of downside that we discovered on the archives end was solely on our shoulders not on the shoulders of the system, which was the need to think very carefully of the kind of tags that are appropriate for this type of descriptive work. We found that very specific tags were much more helpful than broad conceptual tags. Student activities was too broad of a subject term to use in this context, narrowing it to things like football classroom was much more beneficial. I think that this type of photo description could also allow for more non expert intervention in the photo descriptive process, allowing people other than archivists to contribute to this work as well. And I'm going to hand it back to Matt now. Thank you, Julia. We also had some conclusions about software design and user interface design, our observations and user feedback from our archivists and some metadata specialists and digitization specialists who help test out this project showed that again, these were expert users, and having a system that was more akin to what you might get in a 747 cockpit than the sort of simple interface that we might want to present to a public user. Our experts needed that fancy interface they needed ubiquitous and multifaceted entry points into the metadata. So they were there were points in sorting and filtering through the collections where they said, Oh, it'd be great to have visual search here, and also here, and I want to be able to pull up their archival context over here as well. And so we can only do so much within our limited time span of this initiative, but the feedback from UI design was incredibly useful for understanding what kinds of work would be very most useful to these expert users in the future. And what we do as well as data provenance, the ability, not only for our archivists to check the work that the computer had done, but also to check the work that each other had done or to go back and undo mistakes or undo changes of decisions that they had made. And so what we do as well would be having a variety of visual clustering and search methods we only implemented one here, but being able to search on things like overall composition versus more object based detection system could be very useful for certain kinds of photographs, or certain kinds of tags that they may want to apply. And this brings us to some of our implications for actually larger computer vision research.  Of course, that's very different from the data set that we're using, which are black and white photographs coming from cultural heritage collections. There's a need in the field for these pre trained models to be coming from much more from. There's a real need for pre trained models to be coming from cultural heritage collections, historical photographs that might be using diverse photographic processes, rather than just relying on born digital photographs. Like orientation prediction, we'd love to be able to do tasks like classifying the physical source. Did this scan come from a negative versus did the scan come from a print, or being able to predict the photograph. Ultimately, though, we think that the biggest opportunities in this kind of work in visual cultural heritage are not just in creating fancier AI methods with fancier computer vision models underneath them, but instead in creating the user interface systems that connect intuitively with artificial intelligence, using just enough machine learning to help the humans bring their expertise to bear in an efficient manner. So to summarize our key findings, any computer vision project that's going to be happening in archives, libraries or museums really does need a functioning digital asset management system, as well as organizational information about its collection metadata. The automated indescription by itself is really bad, but generic visual search, when it's connected to existing metadata when it's being used in a human centered workflow, can really supercharge the kind of work that metadata specialists do. There is a field wide need for specialized computer vision training sets, not just using born digital color photos, but using historical non born digital photograph collections. To repeat, ultimately, that the user interface design for computer vision metadata systems is just as important and probably even more so than coming up with even more advanced and machine learning algorithms. So, thanks for listening to our presentation. You can find the white paper at this DOI and are happy to be contacted with any questions. Thank you.


Why are there two votes? If you're eligible to vote, then Scottish Parliament elections, it's really important that you do. Why? Because it's one way that you can have your say in how Scotland is run. And on who represents you. OK, but how? We don't have quite enough seats in the Scottish Parliament for the entire population of Scotland to fit. So, instead, you're represented by eight people, your members of the Scottish Parliament. This is how they are chosen. At election time, you'll get two ballot papers. One is for the local area, the constituency that you live in. And the other is for your region. Regions are just larger areas made up of multiple constituencies. The constituency vote is straightforward. You choose the person you want to represent you. The person with the most votes in that constituency wins. Hooray! You might recognise this system because it's also used in UK Parliament elections. This is called First Pass the Post because only one person can win. So, even if it is really close, or they combine votes for all the other candidates is bigger, then it's still just one winner. This is where your other vote comes in. It helps make elections more representative and the final result more proportional to the number of votes. So, with your regional ballot, you choose the party or independent candidate you want to represent your region. This is where things get interesting. The constituency votes are counted first. So, we know who has been elected for all the constituencies in your region. This affects how many additional MSPs a party can gain in the regional vote. We take the number of MSPs already elected and use it in a formula to calculate who will represent the region. There are several rounds of calculations. Each time, votes are divided using the formula, resulting in a new winner to represent the region. These calculations continue until all the regional MSPs have been elected. Still with us? We hope so! The percentage of MSPs each party has broadly matches the percentage of votes they won. Smaller parties and independent candidates still have a chance to be elected, so voters are able to choose whoever best represents their values. You now have one constituency MSP and seven regional MSPs. All eight MSPs represent you, and you can contact any one of them if you need their help. Ta-da! We now have 129 members of the Scottish Parliament, representing all of Scotland.


He was in such a good place before he got injured. You know, it was just, you know, at bat quality was just really, really good. You know, even when he went through a few games where he wasn't getting hits, it was just good at bat after good at bat and obviously the threat that he brings. So, you know, hopefully in these last whatever we're down to now, 13 games or so, you know, he'll be able to build up properly and rack up a lot of it bats and, you know, most importantly go out and help us win ballgames. Why isn't Gleber in the lineup tonight? Gleber is just dealing with a little bit of a quad and kind of wanted to be in there today.  So, you know, we'll kind of go day by day with them and make sure that it's out of there completely before we get them back out. He'll be available off the bench similar to Sunday. Just to go back to Gleber there, what's your level of concern that this is going to be something that he has to deal with for the rest of the year for as long as you guys play? Yeah, I'm not too concerned. I feel like this is something that, you know, we'll knock out, you know, hopefully today or tomorrow or whenever we get back in there. But it's kind of being cautious and trying to be ahead of, so we do avoid an issue. You know, we do avoid potentially an injury.  But I also want to make sure that, you know, as important as these games are, keeping him healthy and playing every day for us in the final days and into the playoffs is obviously very important. With Urshela, what's your plan in getting him back in the fold? I mean, he's in there tonight and should be good to go. I mean, we'll obviously monitor him day by day and just see how he's responding day in and day out as to when he may or may not need a day off. But you don't foresee any planned off days? No, I mean, look, we got 13 in a row. I can't imagine him playing every day. And, you know, I could certainly see some time this week picking a day or whatever, but those will be conversations that we have each and every night with just how he's, how he gets through the game, how he's bouncing back.


Alrighty, so today's conspiracy theory video is going to cause great divide. Where two parks in the Cedar Fair chain battle it out to see whom will reign America's next top park. I'm kidding, this ain't that kind of a video. So today I'm going to entertain the idea that I've heard around the internet about Cedar Point being out of room and growth potential. Along with that, King's Island down the road may become the flagship park. Cedar Island coming to the Ohio State in 2030. I'm kidding. Anyways, so for me, I stand strongly on one side of this argument, but we will get to that at the end of the video. For those that don't know and are new to these videos, you've just clicked onto an amusement insider's conspiracy theory video. So every Monday I take a controversial topic from the internet or group chat and play devil's advocate and talk about all sides of the argument.  I'm kidding. But seriously, comment down below what you think and don't worry, we all have different opinions and that's totally okay. So let's get ourselves back on topic and start with is Cedar Point out of room or close to being out of room? So the answer in my opinion is, well, yeah, kind of. But see, things are never as simple as they seem. We all tend to jump right onto the no more room for growth bandwagon when looking at parks. Yes, parks may have advertised on investor pages that all the acreage is used up, but that doesn't translate into we have no more room for expansion. And as you'll learn later, Cedar Point is definitely not one of those parks that are entirely out of room for expansion. I'll talk about that a little more in detail. So yes, we can all easily look at Cedar Point and cringe at how landlocked it is, but a lot of seasonal parks fall into this category. There's a ton of options open for parks that are landlocked to growth and to expand. And Cedar Point is one of them that has a ton of open doors in terms of possibilities. Now, the part where it gets difficult is the fact that Cedar Point is the roller coaster capital of the world and will continue to build massive coasters that take up a ton of space. So using the open options will end up running them short on available options. So what does Cedar Fair do? Well, this is the question circulating on the interwebs. What will Cedar Fair do when Cedar Point runs out of land and expansion room? They have the resorts, they have the sporting facilities and they most certainly have the roller coasters. But what will happen when they have no more land to grow? Well, that's an interesting topic of discussion because if you look here, it says they have 110 acreage available for expansion. A lot of people are like, well, where is that acreage? Well, it's not on the island. So the land that they're talking about is land that they're purchasing off of the peninsula. I believe it's actually just right when you're starting to drive onto the peninsula. It's where there used to be kind of like smaller hotels or motels and a few restaurants. So that's the acreage they're talking about. They're buying up more land. They bought out that old airport turned it into a sporting facility. They're talking about that land. But there is still land available in Cedar Point, but running out of that land is going to happen very quickly. That's the point I'm trying to make. And that's the point that a lot of people are aware of too. But regardless of that, there are options available. We see other parks doing it and Cedar Fair is no different than any other technical theme park or amusement park. They will find a way around things in the end. So, well, this is probably where I should insert the Kings Island backstory into this conspiracy theory. Lately online, I've been hearing a lot of conversation on Kings Island's 2020 giga strata, flying RMC conversion of Son of Beast, which is totally believable by the way. I'm kidding. And a lot of people are beginning to see potential in Kings Island as the new top dog if things go south at Cedar Point. This is where things get really complicated. So Kings Island is a park with a ton of land and growth potential in terms of being able to pull it off. The only problem you ask, well, from the investor standpoint, Ohio hasn't shown much potential for year over year growth in the last couple of years. And it's not like a major city expansion is occurring anywhere in the Ohio State like you see at other parks in the chain. Well, that doesn't mean they're not going to see an expansion or they're not up for an expansion because of that. As you'll see, Kings Island in our next slide coming up, Kings Island is actually one of those parks that is starting to show some sort of growth. So what would draw Cedar Fair's eye to Kings Island? One, you have the land opportunity for expansion in hotels. 350 acreage available for expansion, that's a lot. Two, it's closer to larger populations, thus events like Winterfest, which has a huge success at Kings Island as you see here, are possible. And three, we are already seeing some sort of expansion work taking place with very frequent investments and a rumored 2020 major coaster. There are a few reasonings to also be on the defense on this idea and they all stem around the concept that Ohio as a whole hasn't shown much year over year growth rates, as we just discussed. The two parks have lacked in growing attendance figures recently. In fact, so much so that even with massive additions, they don't see the same growth increase like other parks experience. I'll know more tomorrow after the stockholders call and release a separate video on that as well. Regardless of the growth rates at the Ohio parks, you can rest assured that Cedar Fair will never give up on those parks. They're extremely valuable to the greater picture and still mass produce great income for the chain. A business never gives up on parks after just a couple seasons of bad fortune. I actually see a lot of potential in Kings Island and Cedar Point and Cedar Fair just needs to break this curse of bad fortune for a better word. So at the end of the day, I truly stand on the side of confused, I really do. So this is where I was saying it gets difficult. So I honestly feel confused by this theory as it's plausible and in fact, it's so plausible I just entirely see the two parks being elite parks in Ohio, both with hotels and resorts, both expanding at an equal rate and neither getting the shaft in terms of investments. However, 2020 will be the tell-all year. Will Cedar Point get the massive coaster? Will Kings Island get the massive coaster? Or will both parks get the massive coaster? I want you guys to comment down below which park or parks you think will see coasters in 2020 for Cedar Fair and what you think about this conspiracy theory. Some truth to it, completely false? Comment below what you think and don't be shy to drag the theory. Remember, these aren't amusement insiders theories or concepts. These are merely discussion we see online and we try to understand them and speculate on them. So for a recap, to go over a couple of the concepts of the theory, people are suggesting that Cedar Point is out of land and there's nothing they can do and hopefully this video helped counter that. So as you saw, Cedar Point does still have land available, they have around 110 acreage and yes, it may not be all on property but there is still land available. People have been suggesting that Kings Island with recent investments and the amount of acreage available could be the next top dog park and yes, that is very plausible too. But what better way to conquer Ohio or set like your flag in the ground of the roller coaster capital of the world than to have two major parks in the same state, attracting people to visit, both parks. In fact, if you go to Cedar Point's website you can buy tickets now to go to both Kings Island and Cedar Point with like a two-day ticket which is actually very genius and it kind of shows you that Cedar Point or Cedar Fair isn't going, well, Cedar Point is our major park and they're so close together let's just invest in Cedar Point. No, Kings Island is definitely gonna get investments as well. Two, you have the whole argument of Kings Island having more land, Kings Island showing a slight more ability to grow their attendance than Cedar Point, that's kind of hinting at like Kings Island becoming the better park. Will Kings Island get a hotel? Kings Island is getting dorms or it's rumored to be getting dorms. So there's a lot of talk going around all those subjects that refer to Kings Island becoming the better park. And I don't know about you guys, I do honestly see both parks being major parks. Right now Kings Island is nowhere close to being as good of a park as Cedar Point. But that's not to say that down the road they won't be anywhere close. Again, if Cedar Point is running out of room they're not gonna be able to build those massive coasters that go out and back like they currently do. And it's at this point in the video where I wrap things up and honestly thanks so much for watching today's conspiracy video. I really hope you enjoyed this one. It's very controversial and I totally understand that. Don't forget to leave conspiracy theory ideas down below for us to cover in future videos as we move out of Cedar Point theories and don't forget to ring that notification bell to stay up to date on all of our content. Don't forget every Monday we release a conspiracy theory video. Don't forget to smash that like button, subscribe if you haven't and share this video to get others talking about this conspiracy theory. Thanks for watching guys and have an amazing night. Bye.


What's up everyone it's your girl Kenzie here from Leighton Sports Guards and I'm ripping a personal for Rory J Last personal of the Sunday daytime shift here. We've got a 2023 top Star Wars signature series. All right, best of luck Rory I will be on drip tomorrow night. Yep Yep, yep. We've got a axed wove Axe wolves auto it's gonna be from the Mandalorian and that is Simon. Oh, man Cassie endis Cassie endis auto Simon woes Alrighty that will do it for you Rory. Thanks again for hopping in and closing us out today. All right. Have a good one


Hello and welcome to the rugged rack hand. La-la-la, I'm gonna try that again. Hello and welcome to the rugged rock hand. Today, I'm cutting some rocks. I'm here in the backyard. Oh yeah.  Let's grab a little bit of it and see what we can find. For starters, we got a little bit of that bird's eye material hadn't cut open yet. This one's probably not gonna be one of those gastropods. Probably something else. Yep, looks like it's a shell, like a little clam or oyster shell. Yeah, that's probably what that is. Next, we've got a little bit of a black agate. And OK, you can definitely see the black agate going through it. Got a little bit of a nice spot right there. All right, not too bad. This is a little bit of that agatized bearite that comes from Yellowcat. It broke open, broke up a bit, but there you can see. So there's still some of the bearite around the edge. And then you can see where it's been replaced in the middle. You got some red agate and a little bit of white quartz right in the middle. So that's really common for out here. Yeah. This is a little bit of limestone that I thought looked pretty cool on the outside. And I wondered if that pattern continued on the inside. Nope, it doesn't. It's just solid limestone on the inside. This is a little bit of agate I found sitting around. I don't know where this came from. I probably found this a long time ago. Oh, that ring structure continues on the inside. Huh, very interesting. Yeah, I don't know where this came from. Pretty cool, though.  OK, that looks like what I thought it would. Not a whole lot, but still kind of cool.  Oh, wait a minute. That's not Doug way. That looks like one of the geodes I got a long time ago from Enterprise. The reservoir in Enterprise has geodes on it. Huh, I think I got that a long time ago. Here's some purple jasper that also comes from out by Enterprise. Yeah, it looks kind of cool. Very purple, though, which is nice. Here's a nice piece of petrified wood from Nevada. And that looks nice inside. It's got a little bit of yellow. The wood out there usually doesn't have yellow in it. That's cool. Here's a little agate nodule that comes from out at Yellowcat. Oh, that looks nice inside. Actually, that's better than most of the ones I get. Oh, look at that center. That is really pretty. Very nice.  It's kind of a faded purple on the outside. Oh, the inside is much better. Oh, that's nice. Yeah, I like that one. Here's some jasper that comes from the Drum Mountains. And oh, nice, very good pattern in it. This is very typical of that area up in the Jasper. Nice. And last for today was this long piece of Wonderstone that I thought was going to break when I tried to cut it. And it didn't look how thin that thing is and it didn't break. So let's take a look. And, of course, it does not disappoint. Look at that beautiful thing. Oh, it's so pretty. Good old Vernon Wonderstone. And that's it for today. Hope you enjoyed. There were some neat ones in there. There were some things that were unexpected. And it's always good to just cut them open and kind of remember places I've been in the past. Anyway, until next time, remember, there is treasure everywhere.


I'm stuck at home, it's been over a month and there's one thing that's keeping me alive through all this ladies and gentlemen. Memes. Very good quality memes. And I'm in desperate need, so I put out a tweet. If I'm gonna make it through the lockdown, I'm gonna need y'all to send me the funniest memes you got. And oh boy, does it seem like you did. Hey, first of all, I should put everyone on notice that Mondays and Thursday seem to be the days, all right? I don't know, I've just been enjoying uploading on all my channels on the same day. We got an A&P swimming video dropping today. Of course, there's a podcast dropping today and I'm also trying to upload something on my second channel today. Hey, links to those in the description. On the A&P video, look at me, man. I was built for this. And I'm finally uploading the full footage of the NBA Live Don't Contest that I uploaded a couple weeks ago on my channel and the Vince Carter video that started to blow up like crazy. Hey, links to all that is in the description. Now, let's keep everybody sane through all of this. Hopefully you guys didn't disappoint with your memes. If you're new to the channel, subscribe. We upload so consistently here. Do yourself the favor, man. Now let's do this, fellas, let's do it. First up, we got Daniel. God damn, your meme got 1.4 thousand likes. Joe knows, uploads a video. Badge plug.  Write that down, write that down. Oh, oopsies.  I already ruined it even though I kind of knew where it was going. Write that down, write that down. We just witnessed a flat out solid meme. It didn't amaze me, but it's still kind of funny. Hey, that's an eight out of 10. When you're on a fast break with your teammate and he dunks instead of throwing the lob. So now we're selfish. Now we only care about ourselves now. Is that what it is? We only care about ourselves.  Turn out. Hey dude, I love dashing man. That's kind of true, I guess. But the thing is, it's not entirely accurate though. Cause ideally if he cared about himself, he would have thrown the OOP cause you get way more rep that way. I mean, who passes up a wide open alley OOP on NBA 2K20? Exactly. So he wouldn't even care about himself.  He's nihilistic.  Because the meme was funny, but it wasn't accurate, I'm gonna have to give you a six out of 10. Surzi sends this meme when agent uploads on multiple channels in one day. Nice. Made me happy, made me smile, nice.   But this one just made him happy and it's nice. It doesn't get more wholesome than that. And I love me a good wholesome meme. Eight out of 10. JFL sends in this one, Ronnie vs. Boogie. Oh no. Oh, Boogie. You call me? Oh. Oh. Oh my God. Damn, that one made contact. You guys ever slap box with your friends growing up and then like one connects a little too hard and everybody stops? This is, this is when you stop right here. Ronnie, stop disrespecting people that rack up 25 technicals a season.  Kidding, of course. Boogie would never do that. I, I don't know, dude. It was like a six out of 10. Don't be honest. No caption needed. Oh, I know the song. Hold on, hold on, hold on. I don't tell me, don't tell me. I don't wanna tell me, don't tell me, don't tell me. Okay. Don't tell me, don't tell me, don't tell me. Can't even, I can't even focus on the meme because it's, flight is too distracting. Oh, there is no meme. There's no caption. All right, good. This ain't a meme, but flight got my Zoom teacher brainwashed.  Oh no. What it do? Oh, oh. Oh, please no. A cringe. Did you guys feel it in your insides? Oh my, she probably was like, this gonna hit with the students later tonight when I hop on my Zoom call. Hey yo, later tonight. Hey yo, husband. Hey, I'm gonna do the flight intro tonight on Zoom. You think it's okay? Yeah, thank you.  Agent on his way to Popeye's as soon as the lockdown is over. The quickness in which I just ran right there. I know y'all seen that. Look. My dog after he got neutered. So where's the ball? Both of the ball brothers are gone. We gotta find where the balls went. Yo, weird. How could you be watching on my league video? And that's the conclusion you came to.   I don't even know what I'm looking at. Flight react transformations. Here he is in his base form, first form, second form, third form, final form. All right, okay. The top response to this tweet is unable to locate the funny. And guys, if you look carefully here, it's a radar trying to locate funny. And he can't do it. It's very unsuccessful. I'm gonna be honest with you, so far today, this is the least funny meme we've seen. I'm gonna give you a three out of 10.   All right, I'm a head out. Oh yeah, I can't. But I was kind of sad and didn't go where I think I was gonna go. I thought I was gonna go this direction, D. And went this direction, a bamboozled meme. And I love being bamboozled in a meme. It means that you thought it out or at least just stole it from someone who thought it out. And either way, I can go to respect it as long as I haven't seen the original meme before it's the first time I've seen this meme. The meme on originality is A1. Creativity on bamboozle is A1. Now if I'm gonna be real honest with you guys, it's not hitting with the funny the way I thought I was gonna hit with the funny. Seven out of 10. 2K, when the patch notes don't get released.  This is one of those memes where you just, you can't help but say it with them, ladies and gentlemen. Why an African group of individual? I'm guessing, obviously guys. I don't know how they identify. Is speaking English is weird to me?  Guys, whoever was there when this meme was, this doesn't matter so much to me because yo, this is truly bothering me. How did this meme grow to exist? Who are these people? And why is it is what it is so infectious to say? All questions I need answered.  It wasn't horrible. Actually, I'm just being hard on you, Tosik. Cause I want you to really improve. It's a five and a half out of 10. Okay, I might know where this is going. Cameron Lee sends in this one. It says 2K WNBA fake as F. So you know, just bored on 2K. I want some WNBA, right?   Hey, cause everyone's bored. We'll take any kind of entertainment right now. So it's packed. Yes, I'm going to show up to a game too.   You can't think creatively, man. It's holding you back right now. I try to stop myself, but I still saw it coming, man. Three out of 10. Ladies and gentlemen, so far with these memes, so good with these memes, okay? Make sure to subscribe to the channel. It's the only place you're going to get good quality memes with a good quality. Talk to your authentic reviewer like myself. Ladies and gentlemen, the devs when we believed Ronnie about a new park. This I hope is creative, bro. I hope it's creative because I know this, this is played out. We don't make been through this meme too many times. All right. Yo, why do I still smile when I see that video? I don't know what it is. Oh, who's calling me? Oh no, we don't answer those. I don't answer those phone calls. Ain't it crazy? Let me just kick my feet up real quick. There's something real inspiring about laughter. I don't know what it is.  When I see someone laugh as hard as the guys in the breakfast club, it makes me wanna laugh. I genuinely, instinctively wanna laugh, man. I don't know fusion. Your meme was doggy dog, man. And I need you to know that, but for some reason I'm still sitting here laughing. Now, isn't that what memes is all about? Laughter? I'm gonna give you a six out of 10. You could tell I'm getting serious with the memes when a drop of sweat comes. You know what I mean? Cause it's, oh it's 80 degrees in the room right now. Let me get that, man. Let me go ahead and get that. Willie, change the thermostat one more time, bro.  Iggy Mo sends in this meme.   How? Just watch the free video. Now, if I seen this on the side, while I was trying to watch my favorite category, I'd have been pissed. I'm trying to enjoy myself clearly.  To the side of this? Now I'm gonna have to full screen. You left me no option. I'm gonna have to full screen. But as it relates to the meme, it's just not funny. Like you could have made it funnier. See what I'm saying? There was more funny, there was more funny potential here. Yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah. Yo, yo, you missed on the potential. All right, man.  I need better from you. My, my player in the first week of 2K20 is about to be generic. Ronnie, your form is horrible. And the fact that he was working with professional, like, I like, there was, yo, yo, who was it? Yo, who was you working with, man? Ronnie, bro, you've been working for 2K for how long?  How was this still your shot?  Yeah, I went to 1v1 Ronnie in the game of In Real Life basketball once this is all over with the Rona. And the winner? I don't know. Y'all think of something clever for the winner to get, but that would be hilarious. And I would destroy Ronnie, like 12-0. When the heartbeat sensor shows three dots, 10 meters away. Nah, yo, yo, I know it's not a 2K meme, but why is this the best one so far? Hey, yo, I don't use the heartbeat sensor, but I know other people use it. I'm just trying to think, like, imagine me moving in SuperStore with my squad, right? All four of us now, we moving in sequence, right? And you know, the heartbeat sensor tells you there's something wrong. Your heart starts to go a little quicker, man. You're like, whoa, around which corner? Yo, I don't know why that's, I explained the meme, I kind of ruined it, guys. I apologize.  I'm gonna have to give you the first ever. The try-hards when they seen Lose had more turnovers than points.   He does, he does, he's pretty bad if we're gonna keep it a bean. But also, come on, it was me in the passing lanes, y'all. When we was playing in that game, it was me in the passing lane. Don't blame Lose, guys, just credit me, right? The meme itself was solid. I'm gonna give you a seven out of 10. I almost ran over all my wires, guys. This whole video would have been ruined. When I do these videos, I try my best to not read a meme before recording because it's gonna spoil it. I try not to look, eh, eh. I just open tabs without looking and I kind of rely on you guys. If you like a meme a whole bunch of times, I'm gonna think it's a good meme and I'm gonna put in the video. I don't plan it out like that. And so I haven't seen this iPod meme, but I'm so ready to give it a negative five out of 10 already. I had to relax, relax, relax, relax. Late submission, reacting to NBA players playing NBA 2K20 tournaments. And it was exactly what I expected, guys. Two and a half out of 10, iPod. This is better than other ones you've sent before, but it is still horrible relative to everything else I've ever heard in scene. JC sends in this one and it's just a TikTok. He sent me a TikTok. Maybe a lot of TikToks out here. Some of y'all, some of y'all need to get a little bit more creative. She, I'm a different breed. She, I'm a different culture. I'm a different man. I'm a different everything. You ain't built like me. You ain't built for me. You ain't built all like me. Yo, yo, yo. That's one of the best memes I've seen in a minute. I was just so enamored by the creativity that I couldn't help but smile, man. I couldn't help but smile. For those of you who don't understand the reference, in 2K14, there was a feature where if you had a mic connected to the game and you swore, you actually got a technical in the game, which was crazy. But they added it into the game and so that's what it's referencing here. And here I go explaining memes again, ruining them. Give this one a cool little nine out of 10. teammate, play some defense. Davis. Rrr. This is an average meme, four out of 10. When they drop the play, an agent has to move from the hash. They would never draw up a play where I had to move from the hash.  I'm a hash sitter at heart. If you don't get that voice out of my face. Exactly. And that's a fact. Get it out of his face, man. I'm a hash sitter. I'm not never leaving this hash, all right? You about to see me open right here, but God.  This is my pride. This is this right here, this hash. This is my kingdom. You not gonna get me to move off the hash. I'm having way too much fun with this one. I'm gonna be honest with y'all. Big Booty Beef Boss sends in, that felt weird saying it out loud, sends in this one, social distancing. And look guys, on one side is Ronnie. On the other side, it's a good two game. And I know that we've seen these social distancing memes, but this one for some reason is still making me laugh. I don't know why, because it has all the homeworks of a bad meme, guys. I mean, look at the format. But for some reason, I'll see it and I laugh. And that's what memes are about eight and a half out of 10. When you tell your crush to rate you and she gives you a four. Oh, that's a tough situation, yo. Hold on, hold on, hold on, hold on. We're going somewhere with these ratings right here. I can't complain, hold on. We can't complain low key. Let's suspect this right now. So we're number four, so number one is bad. You know, like you have so many thoughts rush your mind that you don't even know which one to say first. All right, so let's get into it. Flight, it's not suspect, it's inspect. You're inspecting this, right? But I love the positivity flight. It don't matter what she give you. There's some positive, at least the ones in the one. See what I'm saying? There's uglier people than you to her, which means you still stand a chance. That's scent mentality, ladies and gentlemen. Last meme. Step bro and step dad. Why? Take it out, take it out, take it out, take it out. You don't want that, take it out. Okay, all right, you guys are so weird. Why? All right, this was the video where me and Lil were playing on the NBA Live 2005 old school dunk contest, man. Now, how did you watch that video, which by the way, I've uploaded a full clip on my second channel, link at the top of the description. And then come to this conclusion. You have a dirty mind. Did you know that? Very dirty. Hi, guys. You can subscribe to the channel where we upload stuff and you love stuff. So just be honest with yourself. It's time to click the big red button. I'm gonna catch you guys in the next one. Videos on the screen, everything I talk about, link in the description. Thank you guys for showing so much support. All my channel's doing great. Second channel's doing fantastic.  I'm happy, man. I'm happy. Thank you guys, I appreciate it.