Good day and welcome to Issues and Answers, a production of the Government Information Service. I'm your host, Jacques Hingson Compton, and today we're going to talk about the role of the Youth Department in the Ministry of Youth Development and Sports. And with me is Director of the Department of Youth, Mary Wilfred. Thank you for being on the show. Thank you very much. Good afternoon, Mr. Hingson Compton. I am honored to be here this afternoon. Thank you for that. So let us start off with the role and importance of your department, the Department of Youth. Okay, so it's the Ministry of Youth Development and Sports and there are two divisions, the Division of Sports and the Division of Youth. So I'm currently serving as the Director of the Division of Youth. This division emerged in 2017, January 2017. There was a cabinet decision to split between youth and sports, because previous to that we had a youth and sports director. And so in an effort to place more effort or concentrated effort on youth development, the decision was made to split the two. So when you say youth, what age range are you talking about? Our New York policy has marked 16 to 29 as the target for young people, which is in some major international organizations that would be the age range as well. However, while that is the target, we still have programs that would target below 16, or we can have programs that move as high as 35. But that is the band there, 16 to 29. So could you also talk about your role as the Director of Youth? What exactly is your day-to-day like? Okay, so my role as the Director of Youth is to ensure that there are appropriate programs implemented in the communities that would benefit young people, to ensure that youth development remains grassroots, like a bottom-up approach rather than a top-down approach where we decide what young people want. And so with the implementation of the Youth Workers Program, we have about 22 young people in communities who are serving as field workers. And so they report the needs of young people because communities are different. Some communities are more into culture, some are more into music. Different communities have different needs and they would pass that on to us and we would respond through the appropriate programs. So I do want to get into the needs of youth that you do address, but other than the staff that you just mentioned, what other sort of staff do you have? What is their day-to-day like? We also have, well, the youth workers are supervised by youth officers. So their role would be to ensure that these youth workers are doing their workers' plan. They have been effectively supervised, monitored and evaluated. And also there would be the liaison at the office in terms of resources and technical support for the programs that would be going on in the communities. Okay, so now let's talk about the issues that affect youth that you sort of hinted at earlier. I imagine there are a lot. Can you just talk about what you do have to deal with? Is it things like youth unemployment? Yes, there is youth unemployment. There is also a lot of psychosocial issues that are affecting young people. There is youth looking for opportunities to be able to excel in the skills and the talents that they have. There is homeless youth. Young people are without a safe dwelling.  They don't have the required minimum qualifications in the CXC to be able to get into decent employment. So you have these young people that you now have to navigate and lead them to training programs in order to enhance their skills. So there's quite a lot of young people looking for ways to express their own talents. You know, so they're looking for dance groups. They're looking for I'm singing groups. They're looking for organizations and clubs in the communities where they can express those things as well. So with the challenges comes opportunities as well. So a youth worker may have a camp and young people in a community might say, you know, Miss, we really think that we want a dance club in our community. So you now have to put the wheels in motion. Who's going to teach you? Where are you going to train after you've been training? Where are you going to perform? So you now begin to put those wheels in motion because the young people are saying, we are ready. We love to dance. And so you now have maybe 10, 12, 15 young people engage in something that they love. And that thing may take them to, you know, we never know where that skill or that talent may take them to. So this is what we want to continue to do in the community is the young people express the need and we work with them with that need. So how do you just communicate to them solely through the youth officers? The communication is done in the communities through the youth workers. Youth workers would report to their officers that there is a need and then we meet with the appropriate stakeholders and decide, OK, this is what we can do with the resources that we have. So how exactly do you go about facilitating any of the, let's say, the singing groups or the youth groups? OK, so the youth groups are often youth groups or youth clubs are part of the Youth and Sports Council in the communities. So if a youth group is probably, let's say a youth group is having an election or they've just changed the executive, there are some things that they would want from us. They would ask whether they can get training. Yeah, they can ask whether their constitution is robust enough and what is it that can be done to, you know, ensure that the constitution is written well and they are able to work from it. So we can also invite us to supervise the elections to ensure that, you know, the democracy has been practiced well and so we would give support for that. So we can ask you to, we can invite you to a meeting, look at your constitution and discuss with you some of the areas that you may need to improve. So it's a lot of engagement, whether it's virtual or physical, in order to help these young people move from one level to another. OK, you mentioned something a while ago that I do want to address after the break as we do for our first break. It was what you mentioned about youth homelessness. I think that's something that we need to talk about as soon as we come back from the break. You're watching Issues and Answers. We'll be back in a moment. Everyone says counselling, counselling, counselling. I don't have the right chance. In my case, PS won't buy a single counsellor. Mali Glacia, just yesterday you asked me advice about your husband and we spent over an hour on the cell. That's counselling. I don't know what to do. Just think about it, Glacia. When you have a difficulty with someone, you ask your friends for advice to help you to deal with your problems. But wouldn't you prefer getting advice from a professional counsellor? I hope we're not one of those who think counselling is for crazy people. Don't you know the Ministry of the Public Service has an employee assistance programme they call EAP which is offering six free counselling sessions for government employees? Glacia, why don't you take advantage of it? Really? It's free? Let me call the EAP unit ASAP because I want professional, did you say free? Free counselling. Call the EAP unit at 468-2269. EAP Works, let it work for you. Welcome back to Issues and Answers. I'm your host, Jacques Kingston Compton and we're here today with director of youth, Mary Wilfred. Before we went on the break, I noted that I wanted to speak to you about youth homelessness. Just tell us a little bit about that. Well, it's an issue that we've stumbled on. There needs to be more data collected on it. But there is a lot of social displacement taking place in the society. And I think young people are part of that because they form a very large part of our society. So you have young people, well, there's a young man who loves a particular sport who told me that he doesn't have a place to live.  And it was an issue of being socially displaced, not having grown up with his parents and, you know, just not in a secure dwelling, having to depend on friends or, you know, sleep under people's houses. So this is an area we've noted and we would want to see what it is that we can do in order to respond. Because one of the things young people would applaud any ministry for is for responsiveness. We must be able to respond to the needs. We can't be out there saying we are here to help you. And when the need comes up, you know, we cannot respond. So it may have to have a conversation with national housing, equity. It's really a collaboration because the Ministry of Youth cannot do it alone. We have to be able to collaborate with other agencies and address that issue. But we're still in early stages and right now it's anecdotal. We don't have, you know, any research. But it really piqued our interest in terms of, let's see what's happening with our young people and this issue of homelessness. Because it's not, to me at least, not a surprise that there would be youth that are homeless. Is it a case where they have to reach out to you in cases like that, the youth who are homeless? Yeah, I think they did not reach out, but in having a conversation, I understood, you know, because if you engage in certain things you must ask for, you know, where's your passport, if you're traveling. Documents that seem very normal to everybody and then, you know, this is here, this is there. So, yes, it's something that it's a social problem that we have to begin to address and not let it grow or not let it exacerbate. But because I think even during this pandemic with many losing jobs and, you know, that's certain unemployment, unable to pay rent, so even that too may cause further homelessness. So it is a need for us to begin to, as we move on to look at the impact of the pandemic on young people, perhaps that is an area that we would need to capture. So what sort of age range do you think that that particular ill would span? Do you have any examples of maybe school-age children? No, this young man had just left school. Okay. Yeah, this young man had just left school. Okay. And you noted earlier that you, at least in the future, plan to collaborate with departments like Social Equities. Oh, yes. Is that something that you have done before? Yes, we've done, yes. Yes, we do collaborate on many bad projects, projects through the Ministry of Equity where we were part of, we had the Youth Empowerment Project we've had. Yes, we've collaborated before. So because we serve in, you know, we serve in communities and we need to partner with one another so we can be more effective in impacting our people positively. Yeah. Can you also speak in a little more detail about some of the programs you have to encourage youth into something positive? I know you've mentioned the organizations you start to encourage them to sing, to dance. So what have you? Okay, so we have, I want to start off by sharing on the virtual counseling program. We do provide psychosocial support for young people. This program emerged out of the pandemic when we sent out, you know, we had some consultations and we were asking young people what would be the most pressing issue for them during the pandemic and they mentioned that that is an issue there. They had lots of anxiety over CXC, lots of anxiety about going to university, what then, what happens, you know, my parents have lost their jobs, what do we do? So we initiated that program and it's been well received. Many of our young people have been helped in that program. We also have a music and arts program that is coordinated by Mrs. Sherlian, Sherlian is a well-known vocalist in St. Lucia and she, the aim of that program is to discover talent very early so that these young people can be now, they can channel, you know, they can be channeled into singing groups and they can be, you know, they can be that star and that vocalist or whatever they want to do if it's composing or singing or whatever. So lots of work started at the school where Mrs. Mayers had been helping with the music teachers and her first thing was the chorale, big groups and students, children singing in parts but that of course took a back seat because school was out and we've also been involved in video production for independence. We're now working on a quail production. We have young people writing the script and they would be the ones who will be engaged in this film. So we try to target young people with many different skills. So you'd have the writers, you'd have the actors, you'd have the directors and yes. So that is going on even as we speak. And you have put, because film is a passion of mine obviously. Do you have people who train them in these areas? Well, before the pandemic we did quite a bit of training in film. That was done by Mr. Colin Weeks. And so we invited young people in different parts of the island actually to come in to receive the training and the training was well received by both male and female. And not long after that we're into this pandemic where everything has to be video and film. So I think we did well in that. We were able to get these young people involved in that because even now as we speak the Barbono Secondary, we had a bill program Beckwith International Leadership Development where young people are trained to become volunteers and these young people have been trained in that very filmmaking and they now have the club and made certain ads for COVID and encouraging young people to keep the protocols and that sort of a thing. So this is where we want to go. And we know that the demand is there for young people to, if you have a film workshop, young people are going to turn up. Okay, and just briefly before we go on to our final break, for anyone who wants to get into these programs, who do they contact? Okay, so they can contact one of the youth worker in their community or they can come to the Ministry of Youth Development and Sports. We are located on Miku Street opposite Access, Upstairs Hang-Ten and they can ask for Mr. Nixon Barry or they can ask for Miss Mary Wilfred, Mr. Nyron Taliam, Miss Kisha Mongru, our receptionist. Any of these names would, they would have been directed to the youth division. Okay, perfect. So please come visit us. All right, so we're due for our final and second break. You're watching Issues and Answers. Please stay tuned. Keeping hands clean is important for good health. However, after a disaster staying clean is hard to do, especially if there is no pipe-borne water. Simple things you can do, stay clean and remain healthy are, wash your hands with soap and clean water. If these are not available, sanitizers with alcohol are options.  Wash hands after using the bathroom, changing diapers, caring for animals, caring for sick or injured persons, after handling garbage. Washing your hands is one of the best ways to prevent illness. For further information, contact the Bureau of Health Education at telephone number 460-5349. Welcome back to Issues and Answers. I'm your host, Jacques Kingston Compton and we're speaking with the director of youth, Mary Wilfred. Yes, so we're talking a lot about the role of the Department of Youth in the Ministry of Youth Development and Sports. Now, we've spoken a lot about how COVID has affected the youth, but what about your work in particular, your department in particular? How has it changed how they tackle their own work? Okay, so what has happened is that it has placed us more in the virtual space. So there is provision for all youth and sports organizations to use our Zoom account for their meetings, right? But before, they would call and book our conference room for meetings and so it was a good avenue to get to know who are the leaders, but now we're reducing the face-to-face interactions and so we're saying to youth and sports organizations there may be a cost to the Zoom conference and call. We can give you access to ours so you're able to meet your members. So we can meet them and they can also meet their members. So quite a number of organizations have held their elections, they've held their meetings through our Zoom platform. We want to really initiate a virtual development, a youth virtual development area where we're planning to have a forum where young people can come and discuss hot topics that affect them. That will be run by young people, it will be programmed by them and so giving them a voice to speak out is important to us and so we want to offer that, that would be one of the new things that COVID has allowed us to do. We also want to look at a place where young people in art and sculpting and painting, there's a place where people can see what they are doing, young people, because before you had to go to the town hall to see paintings and you know these kinds of things but now that it's not been done, we're planning or we're thinking of giving them that kind of space. A place where you can look at St. Lucian trivia, so young people are now tasked to look at what is happening in St. Lucian that somebody can go online. You know as you go on Facebook they're trivia concerning rivers and capitals and things, we want that for us because young people are saying, you're not teaching us enough heritage, we don't know enough about our heritage. When Creole month comes there's a big splash but then we don't know our heritage so we're thinking of that trivia on our heritage so we've tasked them to do that work for us so we have a bank of questions where you can just look it up and see what you know about your heritage. So these are some of the ways in which we are trying to use that virtual space so that young people can get information, get involved, they can participate, they can produce that sort of thing. Now this would be a little bit of a controversial topic. I know under COVID you would have an increase in maybe gender based violence but what about youth, child abuse, is that something you all would address? Is it an issue? What I know in some meetings we've participated we know that there has been an increase in teenage pregnancy during the pandemic. That's the only, I suspect the presenter was speaking from a position of knowledge and position of facts so that's all that we know and we've also received data from UNICEF, kind of big organizations that speak to young people facing more violence because everybody is at home, everybody is at home especially when COVID just hit everybody was at home in that small space, crowded, no school because we were still figuring out what it was going on so these organizations have point to increase in gender based violence during the pandemic but we have not collected our own data. It's what I have followed on meetings, on conferences their data is pointing that yes this is happening. Okay we're coming close to the end of the program but I also want to speak a little bit about your relationship with the NYC, the National Youth Council. Do you work in tandem with them? Oh yes, we have a very cordial working relationship with the National Youth Council. We see them as equal partners in youth development and at every turn our intention is to work alongside with them, collaborate their major stakeholder in youth development and so I think all relationships can be improved but I can speak with assurance that we have a very good relationship with the National Youth Council. In fact coming up will be our National Youth Policy Action Plan consultation and there are a major stakeholder in that so I am proud to say yes, we do have a good relationship. Okay and finally if the public wish to volunteer or aid your department in any way how can they do so? So many times there is a call for young people to get involved in groups, get involved in organizations. We've had a lot of vibrant groups that have now become dormant because we don't have the continued leadership and so I want to encourage young people to get involved in their groups. I know the Youth and Sports Council sometimes they struggle to get people to be a part of the executive and we know that there are lots of young people who are capable in joining and adding their leadership skills to ensure that it is governed properly and that it meets the needs of young people. As well there are certain times of the year during youth month, we call on volunteers to help us in committees and help us to execute certain programs that we had. So sometimes it is based on what is happening but the call for volunteerism now is really for young people to get engaged, participate because through that you get the empowerment and through that you see your community through different eyes and what you can do in order to help your community become better. So get involved. Okay thank you for that and we've come to the end of our program. I want to thank you very much for coming on to our program and discussing your department's role and all the issues affecting youth including COVID and everything we've spoken about in the last half hour. So thank you very much. I hope to have you on the program again soon. Thank you Mr. Compton. Mr. Hingson Compton, thank you very much. You're watching Issues and Answers, a production of the Government Information Service. I am your host Jacques Hingson Compton and you can watch this program as well as all our other Government Information Service content on NTN as well as our YouTube channel and our Facebook page. Thank you for watching.


The next panelist is Dr. Graham Williams. You may have seen it on the panelist on the first day. He will talk about ML Hub and accessible machine learning. OK, thank you for joining us. At the end of this fairly long day, I must say pretty exciting day. It's always fun in these conferences. There's lots of idiosyncratic projects that we hear about in the open source community, as well as getting updates on some of our favorite tools, and our previous speaker on TensorFlow. I want to talk about accessible machine learning and this working. It's kind of continuing a theme over a very long career in open source. Most of us in open source, we're very keen to share what we learn, what we build, to share that with the community. My daytime job is a machine learning, artificial intelligence researcher. I did my PhD in the 1980s in AI and machine learning, developing new algorithms, something called decision trees, and actually developed the concept of ensembles of decision trees. And I've kind of been what's called an ensemble person ever since, building multiple models and combining them. And ever since then, I've been looking at how we can, and I've also been an educator, so I've been working in universities teaching for many years. I still teach. I'm still an adjunct professor at the Australian National University, and the University of Canberra. I do some teaching here in Singapore as well. So I'm really keen on this concept of making sure and demystifying, if you like, many of the complexities that we often fear and often see in machine learning and AI. So as I say, this is kind of like what I want to talk about today is a continuation of that theme of how do we make AI and machine learning more accessible to everyone? How do we empower everyone with this kind of technology? Some of you may know of, and I was talking to some in the audience earlier, of some of my earlier work around some toolkits that I, or some products, effectively that I developed called Rattle in the R community, to very easily, it's a graphical user interface, to build your very first machine learning model in less than four clicks or in four clicks or thereabouts. Loading in some data, building your very first model, a decision tree, and exploring and understanding what decision trees are in the machine learning type of context. More recently, I've developed the ideas further, particularly in the context of teaching, and my most recent book, The Essentials of Data Science, introduces a kind of a template approach that practicing data scientists like myself and my team use in a day-to-day basis for the projects that we get involved in. And we publish openly in GitHub a collection of templates, essentially scripts in R and Python that we use as the starting point for any project that we do in data science. And that leads to what I want to talk about today and introduce today, which is mlhub.ai, which is a repository of pre-built machine learning models. Today, particularly in the, you know, today we're in probably the fourth surge of artificial intelligence. Maybe it's the fifth. I wasn't alive for the first, the 1956, Dartmouth meeting that kind of created the whole field of artificial intelligence. But over my career, I've seen three or four surges of interest in AI. This is another surge that we're in at the moment, characterized by massive amounts of data being able to be computed across with massive amounts of compute that kind of is available to everyone today. The supercomputers that we were using in the 90s are now readily available to all of us in the cloud. And relatively cheaply, yes, you're getting to massive GPUs. Some of the compute that we need runs for run weeks on end, that does get very expensive. But for smaller projects firing up, massive compute on the cloud for short periods of time to do some analysis is becoming much more accessible to everyone. However, we are building in this current surge of AI some fairly complex and incredibly useful models in computer vision and in language audio type of areas. Areas characterized by massive amounts of essentially numeric data. A image is numeric data. Audio is numeric data and so on. And that's where neural networks are really doing some fantastic stuff. We don't actually understand what the neural networks are doing underneath. And my purest AI kind of background says, hey, we're not really discovering new knowledge here. But what we are doing is something incredible. It looks like magic. It is producing really good models. And some of these models take weeks of GPU time to build. In Microsoft, some of the language translation model, some of the text processing, speech to text, some of the image processing models that we've built, where we are seeing above human results to some of these tasks, these models are taking multiple weeks of multiple many GPUs to compute these models underneath using TensorFlow, C and DK type technologies. So the question is, how can we, should we be sharing these models? And how can we share these pre-built models, or these models that we've built more freely amongst the community? And there's a number of efforts underway to kind of figure that out. Given also my background in the open source community around back in the early, well, back in the 80s, if you're at the panel, you would have heard I talked about, I explored ideas for packaging Emacs packages and making them freely available as tar files on the internet and developing a repository, then with LaTeX and tech and the CTAN repository, I got involved in that in the early days as well. And then of course, Debian developed its concept of packaging systems. At the time, it was really, wow, this is fantastic stuff. The Debian guys really got on top of how we should be packaging and sharing open source products in an easy way in a repository. And of course, we've seen that repeated over and over again. So now I come to, how can we do something similar for pre-built machine learning models? How can we get them out there for people to access and share the models that we build as data scientists, machine learning researchers, and share those models really easily amongst the community? Make it accessible and freely available so that others can then take those models and build upon what we have there. And we have technology now that allows us to take models and extend and build on those models that we've published and provided. So that's part of the motivation of ML Hub. Another part is, as an educator, I'm often wanting to share or for the students to very quickly come up to speed with technologies. The whole world is not really about neural networks. A lot of work that we do as data scientists is still a lot of the traditional machine learning algorithms, decision trees, are still widely used in a lot of enterprises today. It's a good technology. They're there as random forests or boosted gradient boosting type algorithms. But the technology, which has been around since the 1970s, neural networks have been around since the 1950s, the technology is solid and widely used. We wanted to communicate that kind of technology very, very quickly as well. And to show that it's not really, you know, the mathematics behind there may be some complexities around that. But there's a level of understanding we can gain fairly quickly about how machine learning actually works through a hands-on experience. And we wanted that experience to be a five-minute experience. And I guess this is also my lack of staying ability, I guess. I often see new projects. Somebody shows me their GitHub repository. Often at a conference like this, I'm sitting in the audience and they're presenting a new algorithm. And they say, hey, we've got this on a GitHub repository. And during the presentation, I go to the GitHub repository. I download it. I try to compile it. Oh, I need this and that and get the dependencies there. And I give up if it takes more than five minutes. Maybe it says something more about me than the software. But gee, I really like that experience of being able to take something. And this is true in the Linux world with package managers. I can take a package, apt-get install package, try it in five minutes. If I like it, I'll keep it. If I don't, I move on. So it's that kind of concept I want to try and capture in the ML Hub as well. So there are a number of efforts around already to kind of see if we can create repositories of pre-built models. There's a couple I've got on the screen there. ModelDepo.io is a really nice open source attempt to do this. Nice graphical user interface. Microsoft has, we have our own gallery.azure.ai for a whole bunch of pre-built AI-type models, some of which you can access via APIs to the cloud. First one, modeldepo.io gives you some API interfaces, pretty much aimed at developers. The extra thing we wanted to do with the ML Hub was to make it accessible at a command line to anyone, not just developers. And maybe it's another sign of my heritage. It is a command line tool as a basis at the moment. So everything I'm doing here is controlled through the command line.  And it would be a nice experiment to see how much difficulty you get. And the first difficulty is going to be the tool is currently fairly focused on running out of the box on Ubuntu, and particularly Ubuntu 18.04 LTS. However, you can install virtual machines, et cetera, running Ubuntu. We kind of suggest we test this regularly on Mac OS with parallels with running Ubuntu 18.04 there. How many in the audience actually are running Ubuntu? OK, good number. And how many are running 18.04? What are you running? 18, 10, 19, 04? Not quite. OK, it probably runs out of the box on 18.10 as well. So I would encourage you, particularly if you've got Ubuntu, it's not going to run out of the box on Mac OS, unfortunately, sorry. But if you've got parallels or virtual box or even a container for Ubuntu 18.04, it works on all of them. So to run, to install ML Hub, it's a PIP3 install. So PIP3 install ML Hub, and that will just download ML Hub, latest version, and you'll have it on your local machine. Oh, another way we often run this, which I probably should mention, is through the cloud. So Ubuntu servers on the cloud. You can fire up an Azure, Ubuntu 18.04, or an Azure data science virtual machine, particularly a data science virtual machine, which is a Ubuntu server that we have published on Azure, which contains all of the open source software by default that AI machine learning research to use. So five minutes, push a button, and you have a data science virtual machine that has Python, R, TensorFlow, CNTK, every package that you can imagine that a data scientist uses that is open source, plus a few add-ins from Microsoft. So very easy to install out of the box. The nice thing about the data science virtual machine is all of the dependencies are already there for most of the models that we publish with ML Hub. PIP3 install ML Hub, depending on how familiar you are with PIP and the Python ecosystem, PIP install will install that into .local bin ML, so you need to make sure that's in your path. After you've installed that, you should be able to do just ML or ML available, and that will go to our ML Hub repository and tell you the models that are available at the moment. ML installed will show you the list of models that you've currently installed. Now, the installation process, it's fairly simple. It takes the files of the package. So you can think of this like installing a Linux package. It takes the contents of that package. It unpacks it. It puts it into a .ml Hub folder with the package name. Very, very simple mechanism. Wanted to keep it really simple to avoid too much complexity. And then the ML command will work with what's been extracted into that package folder. So when you type ML available, did anyone actually have success just then? Great. OK, we've got a few hands coming up. So do feel free to give it a go. ML available will list a collection of the packages available on this particular repository. You can point ML Hub to any repository and it will list the packages available on that repository. And there's a collection of sample models there, if you like, that we use to illustrate ML Hub. Now, the kind of hello world example is the rain package. So if we go through that, ML install rain. So that will go to the repository and install the rain package. Now, the first trick here to explain is that we actually decided not to create packages. I'm installing a package, but we don't actually go out and create a package. The packages are created dynamically from GitHub or GitLab or Bitbucket, whatever your favorite Git repository is. And it's all based on YAML files, which is a configuration file, to specify what has to happen for ML Hub. So we were at first packaging stuff from GitHub into a zip file or a R file and having that stored in a repository. That turned out to be quite a losing game. Why take stuff out of GitHub and put it somewhere else? And why not rather take it directly from GitHub and build the packages effectively dynamically? And so you can see here it's actually getting the code from my Git repository, a package called rain. And it's downloading that GitHub repository and unzipping it into .ml hub slash rain. That's the default behavior of ML install. That's all it does. And another thing we've been careful to do is to always give you a guide what to do next. I never like the tools that, OK, we've done something, what do I do next? OK, look up in the manual and see what I do next. And so we've tried to make this as user-friendly as possible in the sense of, where do I go? One of the nice things about Ubuntu these days is if you mistype a command, it will do that. Maybe you meant this command. Or this command is not available. Install it with this app get command and so on. So it's that kind of thing. So it suggests that ML read me. So if you say ML read me in the name of the package, it will show you a little bit of an introduction to that package. In particular, it should give you a link to the actual GitHub repository as well. And for those who are online, have a look at that GitHub repository and you'll get a bit of a sense of what it's doing to turn a GitHub repository into an ML Hub package. And we'll talk a little bit about that as time permits. And then it suggests the next command is an ML Configure. Yeah, dependency is a real issue and a real struggle for it's the real problem trying to solve effectively. How do we make sure we've got the proper dependency so that we can run this? And so we have a system of specifying those dependencies, whether there are dependencies, Python, operating system dependencies, and so on. Whether you're using PIP install or you've got a condor environment in Python or you've got an R environment and you've got a local library of R packages and so on. We're looking at simplifying or handling all of that. So the next command you would run is ML Configure. After the configure, well, it's gone through. This package actually uses Atrial, one of the, it's just a PDF viewer, a collection of packages that it needs from R, so it's an R model in this example. And it ensures that all the dependencies are actually there, available and installed. Next command it suggests is ML Commands Rain. It tells you what are the commands that are provided by this package. So we've got one, two, three, four commands here. Demo, every package should have a demo command. And to be honest, it's a demo.py or a demo.r script that it finds in that GitHub repository. That's all the command is. So we've got a print.r display.r and score.r from that repository. So the next thing I want to do is to run Rain, to run the demo for Rain, ML demo Rain. So I might just swap over to actually showing this if that's visible. Hopefully you can see that as well. So I'm going, I've already installed it, so I won't install it again. ML, let's jump right to that ML demo Rain. Now, a little bit of a description. So I would use this to explain to people what a decision tree model is. Very simple machine learning model. It's predicting whether it's going to rain tomorrow based on historic data. Very simple data set, very simple example. But this is a pre-built model. The concept is we have a pre-built model here that predicts whether it's going to rain tomorrow. And we apply that model, that pre-built decision tree to some actual data, get the actual results and predicted results. And you can see it's mostly getting the right answer. So the model is doing OK. And then I introduced the concept of a confusion matrix. This is a measure of the performance of the model. And we can see it's getting an overall error rate of 25%, average class error, 25%. It's kind of OK. It's not too bad, 75% accuracy type of model. Here's a performance evaluation chart. And we might explain that a little bit. And that's the end of the demo. However, there were some other commands. And it says next, we might want to print. So ML print, and maybe the choice of names can be better. Print is a textual explanation of the model. I won't go into any detail, except to say that textual structure that you see there is just a text representation of a decision tree model. It's discovered that model. That's the model we're using. Some people prefer a more visual version of it. So that will pop up a graphical version of that textual model. So here's the model that we've pre-built on some historic data. It says if the humidity at 3 PM today is, let's say, greater than or equal to 67, then the chance of it raining tomorrow is 73%. Similarly, we go down the other path. If the humidity is lower, but we've got a lot of sunshine today, chance of it raining tomorrow is only 16%. So we predict that it won't rain tomorrow with 84% accuracy, and so on. That's a decision tree model. It's built that model on some historic data and made those decisions. And often we explain the knowledge discovery or what is most impactful on the decision here, whether it rains tomorrow. We have these plots, and it says humidity at 3 PM is the most important variable. Not so interesting from a single decision tree, but very useful when we have something called a random forest or other ensemble approaches. And then we have this particular package also has a score command. So we say in our score rain. And that just extracts the variables from the decision tree, asks me what are the values of some of these variables. Let's just put some random numbers in. I've got no idea what I'm doing here. It's probably a bit too big. And it says, I predict the chance of rain tomorrow to be 43%. If I run that again and put in some different numbers, let's just put in, it's the same. Put in some different numbers, always 43%. Let's try something dramatically different. OK, it's not really showing the effect. There are other pathways through that tree to get down to 16%. OK, so different numbers will take you down different pathways in that tree. This one predicts 43% rain tomorrow. This one 16% rain tomorrow. And then a nice message at the end. Thank you for exploring the rain model. There are no other commands to explore here. So if we go quickly go back to here, we've just gone through all of that. So that's a fairly simple model. That's kind of like the Hello World model. The next one is a colorize. It's a TensorFlow built model. It's one of the traditional examples of using deep learning. It takes black and white photos and colorizes them, turns them into color photos. So this is a pre-built model. The model itself took in thousands. The read me will probably tell us. So in this case, we would go to demo install but then read me colorize should give a little bit of an explanation there. So this was a model built by one of my colleagues in China. Not a lot of extra detail there. If you go to the website, he's probably got more of the details there. It's a TensorFlow deep learning model. Thousands of photos, black and white and color examples and builds a neural network model to take any black and white photo and colorize that photo. So if I look at, so I do an ML install colorize, you should be able to do that. You should then do an ML configure colorize. Now I say within five minutes, you should be able to get the demo up and running. If you don't have all the dependencies there, sometimes that time may take more than just the five minutes. But ML, once you've got the configuration, if you don't have TensorFlow installed, it will install TensorFlow and that's not a trivial process. So it will install the dependencies. So ML, then we go, ML demo colorize. And so the point that the aim here is what could wow you within five minutes? So this goes through a collection of photos, black and white photos that it provides in the package and it colorizes them immediately for us. This is not canned. This is taking those black and white photos and colorizing them as we're running it here. So I'll close that one and bring up the next one.  I think this particular model has a bias towards water and green scenery, I must admit. But it does a reasonable job on most of these. So I'm just going through control, closing the graphic window and it goes on to the next one. And that's hence colorized a whole bunch of images for me using that pre-built model. So an ML install of this actually downloads from his GitHub repository, his code, his demo.py script basically, which and also the configure also downloads his binary model that he's built in TensorFlow already and it downloads that to the local machine and is running it on this local machine here. That model could actually come from anywhere and there are TensorFlow based model repositories. It could come from the TensorFlow repository. Similarly, we've got the ResNet, if you know the ResNet models, we've got those models we can download. We've got some examples of that as well. We'll probably run out of time to see that, but we'll see how far we go. Now I won't do the print colorize. It doesn't really give us much information, but if I do ML, let's say ML read me colorize. Let's just open this link in the browser. So this is going to the GitHub repository. And going to that GitHub repository, you can see it's got a bunch of the usual stuff, but there's this mlhub.yaml file and that's the key for turning any repository into a mlhub package. So the aim is to be non-burdomsome, to be simple, in anyone having a package, anyone having a GitHub repository, installing a, or creating an mlhub.yaml file to specify what is needed and possibly creating this demo.py and particularly score.py for, and that turns it into an mlhub package. mlhub will look at that GitHub repository and in fact on the mlhub command line, mlinstall, you can give it the path to the GitHub repository and it will look for the mlhub.yaml file, download that file and follow the instructions in there. If you're online, you can have a look at this yourself, but if you have a quick look at this, it's a specification configuration. So you can see there's the dependencies, if that's big enough to see, there's the dependencies for this particular package. Yeah, so he is using TensorFlow there, so we have to make sure we've got TensorFlow installed, a whole bunch of other system dependencies plus the files from the repository that we also need to download. So we don't have to get the whole repository, just what's required to be able to demo this particular package. And so you can see a list of the files there and folders plus you can specify, so here's the model that we're downloading. So he's got this in a store somewhere, that's the model that he's built, a HDF5 model, and we just store it locally on this machine once we download it. mlhub will try and be a little bit clever in case you need these downloads when you get the new version from his repository. It won't necessarily download the actual binary model file unless it needed to. So that's what's required to create an mlhub package. Have an mlhub.yaml file on your repository. Now notice another command he's got there is score. Actually, let's make sure I'm not skipping over too much. So that's the colorize package, and we've seen that and we saw the demo and we've talked about building the actual package, so github and having a look at the mlhub.yaml file. Now some github repositories have multiple functionality or multiple commands if you like. Before I say that, let's just think about that mlhub score. Sorry, if I just go back to here, what I wanted to say was there's an ml score and this was colorize. You can provide here an image, any black and white photo that you might have or anything that you might find on the internet. Whoops, I think we've got an example here of a file to colorize, yeah. So you can do this, so I'll just grab some picture from the internet and do that. So that downloads that picture, it colorizes the picture, it will pop up the result of that colorization. So it's not dramatic, but it does do the task. So in effect, you can think of it as a command now.  If I've got a folder of black and white photos that I want to colorize, I just point it to the folder and it will colorize those for me. The aim again is a common infrastructure that we can use to have this working as quickly as possible. So another example is that I might just illustrate with is objects, ml, few ml install a package called objects, it's again another demonstration of using neural networks on images to identify objects in photos. You ml install, ml read me, ml configure objects and then you'll get to ml demo objects. So let's just jump straight to demo objects. This is using the ResNet 152 model. I won't go into details of what that is, we can go to the GitHub repository and see what ResNet and 152 is, but it's a model that will take a photo and identify the primary object in that photo. And there's been competition over the years, maybe over months, between Google, Microsoft and others. And we keep leapfrogging each other on how accurate these type of computer vision models can become. So in this example, here's a computer vision model, it's a pre-built model, so I haven't built the model here, it's already built. If I remember right, I think this is a CNTK model and which is another deep learning framework and it's recognizing in that image that that's an African crocodile. The green there is just the strength of that recognition. It could have been an American alligator or a Komodo dragon, but they, if you see the text here, 99.9% African crocodile, 0.07% that it's an American alligator and 0.02% that it's a Komodo dragon. So that's accurately identified that. And so we've got a collection of examples here. That's a lynx, apparently, rather than a leopard or a snow leopard. This next one's a brambling rather than a partridge. And you can see there, it has slightly higher probabilities that it might have been a partridge or a water loosal. That's a liner, it could have been a dock but a planetarium, but pretty unlikely. This one's a sports car or a racer with some degree of probability or a convertible and so on. We've got a, that's a so-called injury. Could have been a Madagascar cat or a koala, fairly unlikely. And there's a summary of them all. So there's that five minute wow or hmm, looks interesting, but I'm not interested particularly. So I'll move on, I've only wasted five minutes of my time. So that's the kind of intent of building the model. And of course, this has a ML score function. Again, if I do the read me, objects, go open up the GitHub repository. I think we've got further examples there, yeah. So I can score some random images, your own images if you like or images from the internet, copy that, paste that into there, whoops, lost the M. So that's downloading that image, whatever it is from the internet and it will run that model, the ResNet model over that and it comes up and tells us that's a damsel fly, could be a dragon fly, a little bit higher probability in that instance, but in this case, it's clearly a dragon fly. A damsel fly. Okay. One last example, I was saying that we look for the mlhub.yaml file in GitHub and that defines a mlhub package. We can have multiple packages within the one repository. So Microsoft, we're developing what's called a collection of recommendation tools, recommendation packages to support the Netflix type recommenders, food recommenders and so on. We're collecting together a framework for dealing with recommendations. That's all open source and it's built on our experience working with a large number of customers, some of the big customers that you know of who are developing these type of technology. We take the algorithmic side of that and the process side of that and turn it into GitHub repositories that we call best practice that we're sharing openly in GitHub. One of them is the, you will find in my repository called recommenders but the original one, I've got a fork of it, but Microsoft slash recommenders for example. Now there are multiple recommender algorithms and there's something called SAR, which is smart adaptive recommender, there's RBM, which is the restricted Boltzmann machine, there's ALS for alternating least squares and so on. And so there are multiple packages if you like in this single repository. And so all that I've done to the Microsoft repository for recommenders is added, I've added a folder here in this case with two YAML files, RBM.YAML and SAR.YAML. So I can now install either a RBM model or a SAR model from this one repository. And indeed we can do ML install SAR that will download the appropriate files. If we look at one of them, YAML, RBM.YAML for example, you can see that it specifies here the actual files that it downloads. So RBM.py becomes demo.py in the package. Hopefully you can see that. RBM.MD becomes readme.MD in the package, things that ML Hub knows about. In fact, and then there's the actual some data that it also downloads. So if we run this demo, so this is a movie recommender, it's using public data, the movie lens data set if you're in the recommender space. That's the data that is often used. So we're loading the data now. Takes a little while to load that data. It's a fairly large data set, 100,000 users in this data set, it's loaded it and it's showing us the first few data items from there. So you can see a user, the movie they watched, the rating they gave, the movie in the name, the title of the movie. I'm now building, in this case, I'm actually constructing the model and I'm applying the model to this data set. And then I just, as an example, I use it for one particular user. These are the movies that that user has watched or the top five movies that they've rated that they've watched. The images are just downloaded dynamically from Amazon IMDB. And these are movies that are being recommended by the recommendation system, the model that we've just demonstrated here. Again, the images are downloaded from Amazon IMDB to do that demo. So again, wow, that's great or not very interesting and move on to something else. We also do model performance in the framework. So if you're familiar with recommenders, the map is the usual discriminated, but we've got precision recall and so on there. So that's basically what we see in a little bit. We've got face detect models there. Again, a lot of the traditional examples of some of the more complex neural network, deep learning models are included in ML Hub. You can use it live. So ML live face detect. So you can have your camera on your computer start up and it will follow your face around live as we do that. And there's a variety of other packages available. So just to finish, mlhub.ai is where you should be able to find all the information. It's a little experimental still at the moment. I'm really keen to get feedback, comments. This is a crazy idea. Stop it now and move on to something more interesting or hey, this looks interesting. We'd like to contribute a model to it. How can we do that? Love to work with anyone who's interested to do so. And contributions are very much welcome. Thank you very much. So some questions. Hi, Kate, I have two questions. First is, so other than doing just demos of various models, is do you still have the use case of doing ensembles on models somehow to basically leverage all of the underlying models? In terms of the tool itself, it can be used as a so ML score and then a model name. And then you can provide a data file to apply the model to that data file. You could orchestrate it to have multiples of these to save the output and ensemble it in that way. Or you can package it up to demonstrate the concept of an ensemble of different types of models or an ensemble in the decision tree and the ensemble of the same type of modelling approach. So it's not excluded that they do that, but it's not directly supported. And sorry, just a quick second question. Are your slides online somewhere? They're not at the moment, but I will. Keep an eye on mlhub.ai and I'll put them on there. Thank you for your presentation. Just a quick question. So you said that contributions of pack models are welcome, but what's your criteria for curating the models? So currently, and it is being developed and in fact, we moved away from the concept of curation when we moved from taking the code from GitHub and putting it into our zip file and moving that to GitHub itself. Now, in terms of our package repository, so in mlhub, when you say ml available, it gives you a list of the packages. That is curated there. That's where our curation takes place. However, you can install models from anywhere you like. So ml install and then you give it the GitHub location of the YAML file. Anyone can install a model in that way. The ones that we put into our repository, we're curating, we're ensuring that they look OK, they work OK. Hopefully there's no issues in the security of those models. We will review the code and so we are confident the ones that we're curating in that list. Underneath, when you do ml available, you'll have seen a list of, I don't know if I've got that on the next slide. No, you'll see a list of models. Each of them is just a map to a GitHub repository where the model actually lives. So it's just a shorthand for that command line that says ml install and then the whole GitHub path. But that's what we're curating. But there's nothing to stop anyone creating their own ml hub model and you'd be able to point anyone else to that model through the ml hub command. I think that's the end. Thank you very much. Thank you.


When I first saw this article, I thought it was the next battle in the war on meat. Unfortunately, I was correct. A quick Google search reveals a media blitz of dozens and dozens of articles to let everyone possible know that animals were infected. And there is zero proof that one species can get a different species sick with their particular illness.  But it's not like a specific disease that goes into your body that's not possible. This is 100% a lie, just like everything else in the news. Mainstream media is simply the lying psychopaths in charge using puppets to further their own agenda. Don't believe anything they say, just think about what their intended purpose is from like a sicko-tyranical dictator perspective. Here, it's pretty obvious, you know, establish the fact that this can be spread. Then establish the idea that it can transfer from human to animals or animals to humans. If people believe that, then when they come for the meat supply, likely beef, it's going to be accepted.  I've spoken about this happening several times over the, probably more than a dozen times over the past two years, but this is just the continuous predictive programming, preparing people so they don't question anything when it happens. The climate stuff is a big one and I'm just going to stop talking about that all together because I just get censored like crazy. Deer in four states have been exposed to you know what USDA study shows. What does that mean for humans? The USDA study showed that 40% of deer tested across four states in 2021 were positive. None of the deer in the study showed clinical symptoms according to the USDA. The USDA came to no conclusions about how the deer were exposed. It's possible they were exposed through people, the environment, other deer or another animal species. I mean, everyone should be concerned if the USDA is conducting these studies, which means the higher ups or whoever tells the USDA what to do are complicit with the New World Order agenda. Every single piece of meat on a federal level needs to pass USDA inspection to be sold in the supermarket to the general public. They're literally the feds knocking on your door if you do the wrong thing and whether they come for meat in general or try to zone in on specifics like beef, red meat, you can bet that whatever the government wants, these guys the USDA will cooperate with. Whitetail deer, a species found in every US state except Alaska, appear to be contracting the you know what in the wild according to the first study to search for evidence of an outbreak in wild deer. You know what else isn't in Alaska? Thousands and thousands of new cell towers, which clearly aren't being used for cellular signals if they need to install an entire new network and grid when everyone already has a perfectly operating cell phone. Every time some meat industry issue is brought up, there's a direct correlation to environmental radiation or technology. And definitely check out the few videos I did on that last year if you missed them every few months or so. So there's a few mainstream thoughts about this. One, you know the average person thinks I'm crazy. Two, the skeptic might say that it's for your own good as meat is obviously bad for us and the planet. And three, you know a good portion of people is seeing through the power of hungry dictators trying to control every aspect of our lives. You know although politics is fake, republicans and democrats da da da, the republicans you know complain that the leftists want to take away their meat. So they're onto it to some degree although the dividing counter of tactics of fake politics have grasped for them. And although I've spoken about this a lot, I haven't specifically explained in detail in a video like this why they don't want us eating meat. In a way, every aspect of your life is influenced by what you put in your body.  You know the toxins in our food and water poison the physical body that is supposed to produce hormones, endorphins, all sorts of chemical compounds that make us feel good or bad.  And since the food supply is also riddled with so much estrogen, even the meat, most men become low testosterone passive soy boys. And the male-female dynamic here, it needs to be discussed because men are the ones that are likely to rise up and go against authority or as women generally need to be guided and they're more nurturing and loving. Two very different brains, biologically speaking, there's a reason they're putting estrogen in the water and not testosterone. The irony here is they're trying to limit your consumption of highly estrogenic feedlot beef to a lesser extent pork and chicken. And the only reason I can assume is because the pork and chicken are much higher in omega-6 and toxins, even though brain-thread, cornstuff, grade-A, prom-angous beef is not the best for you, it is better for you than chicken or pork, not that any of them should be consumed. Either way, you're removing highly available animal protein, B vitamins, crucial for neurological function. I joked in a lot of videos that vegans have to make cuts every two seconds to remember what they're saying and that's a severe B vitamin deficiency causing memory issues. As you can imagine, someone who doesn't have the vitamins and the nutrients to remember what they said a few seconds ago is very easy to control. Robots will not take over our jobs, we will be the robots needing to be prompted every second by a microchip in our brain. And another reason that beef might be the target is because when people consume like cheeseburgers and hot dogs, a lot of the meat that people do get in their diet is beef-based, so the crazy psychos in charge probably did some complicated math to figure out, okay, if we take away the beef they'll be as stupid as we need them, so I guess, you know, I mean, look, guys, I work in the meat industry, it's my business now, I feel obligated to talk about this stuff whenever it comes up, even though it seems repetitive, because there's going to be a day where, you know, unfortunately I told you so, but I'm trying my best to do what I can, so thank you guys for joining me. If you'd like to support me, you know, we have all the businesses available on frank-defrawn.com. I definitely watch my video tomorrow, the Frankie Surange Meet vlog, and I'll give you guys an update on everything, but outside of that, if you could please drop a like on the video, leave a comment, say hello, subscribe so that YouTube can unsubscribe you next week, and check that notification bell so they don't notify you of my videos. I feel really like, like I need to run through some drywall because I made this, I'll do a video on Saturday, a new, like, Gatorade Powerade drink with the flex straws that we launched last week, I just feel like I need to do something.


Hello everyone, my cousin is here today, link to his channel down below. We have been playing the new Super Mario 3D All Stars, which is the new Switch game that surprised me extremely when it was announced, and it includes Mario 64, Mario Sunshine and Mario Galaxy. For me, I have to say, that this was a dream come true. I don't think Nintendo could have done it better for me especially, because these three Mario games are my favorite Mario games. I also have Mario World now on the Switch, so I am good. And Odyssey, let's not forget, Odyssey as well. So, the Nintendo Switch now is my ultimate Mario console. What do you think about this trilogy? I like it, better pictures, story are the same. This game has been my favorite. Yeah, you have previous experience with Mario 64. Yeah, I've never played the other Mario games. When I was 10 years old, you came over and saw the game? Yeah, I saw the game and tried a little bit. Because I got Mario 64 when I was 10 years old. That was in year 2000, so yeah, I was obsessed with the game, it is so good. But the thing is, going back to Mario 64, the controls, I had forgotten about how the controls were.  We were used to these controls back in the day when we were kids. But now, with a lifetime of gaming, a bunch of games, and following the progression that the gaming world in itself has evolved over the years, controls are just different now.  It's kind of hard to go back to the game, because of course my memory is coming back to me, that this is what we dealt with back in the day, but Mario is kind of hard to control. And in general, games were harder back in the days than now though. Yeah. But I am loving going back to Mario 64. I'm having such a nostalgia trip with it.  Like the ones in the clock tower and all the late game stars. So it's so much fun to go back to the game and truly being committed into fully completing the game over again with 120 stars. How many stars have you collected so far? On my Switch, I think 3-4 stars. Yeah, we just started the game. We got this game yesterday. I'm still waiting for my copy, so we played on your copy.  Now, I want to give some critique to Nintendo actually. When they announced this game, they put a high pressure on the fact that this was a limited game. First of all, why would you make a Mario game limited in the first place? You don't need to do that. It's Mario. It's the king of video games. Second of all, we were all going crazy when we heard that it was going to be only limited printed, limited in physical copies, and also limited in digital. I can't believe it. I just can't believe that. So the thing is, I went on my Discord server and I was like, oh my god, I'm never going to get this game. It's probably not going to come out in Norway even. So I got someone off my Discord channel to buy the game, to pre-order the game from Best Buy in the States. And I pay paled him. I paid for the game. The thing is, he's going to get it first and then send it over to me. But the thing is, this game had a release yesterday. We were out shopping. We went to the electronic store. What do we see? Super Mario. I didn't even think that this game would come to Norway. I mean, the way that Nintendo sort of marketed it to be so limited that it would be hard to get. So I thought I was being smart by pre-ordering it through someone and just ensuring that I would get my physical copy. But in the end, you just walk in there and grab your physical copy in Norte Norway. The shelves were full of this game. Yeah. The first time of my life, I have one game before you. True. That is so true. How does that feel? Weird. You always have the game before me. I don't. I know I do. We came home here and we did a little live stream.  The controls in this game is probably not... I'm trying to just take the shortcut over here, but he slides. When we learned English in school, vegetable was such a hard word to understand. So I said vegetable.  Now, Irene, what kind of table is that? What kind of table is that? Vegetable. It was a great live stream. I really enjoyed it. But I had some technical difficulties. For one, I used a microphone that was built into the webcam, which is not good. And the resolution was 480 for the entire stream. I'm not going to repeat that. Now I have figured out what the settings are going to be in the OBS. But people said it was good and thank you so much for that. Thank you for all the support always. We had a blast yesterday with the playing this game online. I mean live streaming. There was over 130 people in there at the same time. I've never had that many people watch my live stream before, so it was kind of surreal. Yeah, I think it was fun. Yeah, I loved it. I loved it. I had a lot of fun. Also just seeing your first impression on the next game we're going to talk about, which is Mario Sunshine. You played this game for your first time, am I correct? Yeah. Because back when I was 12-13, which is what I think I was when I played Sunshine for my first time, I think you mentioned that you came over to our place back then. I came over to you to see that game, but I've never played it before. I think it was a good game. I will try Mario Sunshine, see if I can complete that game. Yeah, it's definitely worth it. And it looks so good. Now, when it comes to the look of the games, Mario 64 looks the same on the sharper, but it doesn't have a widescreen. I didn't understand why they didn't do that in widescreen. But anyways, it looks good. It plays good. End of story. And Sunshine, they made it widescreen and also made it crisper, sharper. They're like new games almost, because I can really feel and see the improvements with the remastering that they did with these games. And Sunshine looks really, really beautiful and so good. It is a game where Mario comes to a tropical island and he is being blamed for vandalism on the entire island. And you have to clean up, because there's actually a Shadow Mario that did all the messing around with the paint and all of that, but you have to clean it up anyway. You also collect 120 stars in this game. It was also real. The nostalgia when it hits you is so unreal. I love it. It's like I'm a little kid again and this is what I'm living for, sort of thing. The feelings that video games give me, it is amazing. Now, we also dialed a tiny bit into Mario Galaxy. What was your first impression of that? Because from what I remember you have never played that? Never played it. Have you ever seen Mario Galaxy before? I was visiting you and I... Yeah, you visited me when I was 16. And it was a little weird with the planet, all that. Yeah, the gravity. Playing gravity in that game, I love it. Yeah, that was a little weird, but I think I will try that. Oh, it's so good. Actually, Galaxy is probably my favorite out of this pack. Even though my nostalgia is hardest for Mario 64 and also quite hard on Sunshine, my favorite is maybe Galaxy, because of how much that game blew me away when it came out on the Wii. A new console, I remember. It was my second game on the Wii, the white one, in 2006. Amazing. So I'm gonna play that game together with my mom, because she has repeatedly over the years talked about Mario Galaxy. When are you gonna play Mario Galaxy? So that is also a very parent-friendly game, apparently.  So yeah, we have a great first impression with this collection, and we highly recommend it. Now, I wanna say that I don't understand the whole limited marketing that Nintendo is doing with this game. Never understood that. That only made people go crazy. Scalpers gone crazy on eBay, and it being sold out everywhere, apparently, but it isn't. So I don't know, it's so confusing. Why do the limited thing? It's only confusing, and it made us go crazy for a moment. So we are going to definitely be enjoying this trilogy. I can see myself playing this for the rest of my life on and off, because it is my childhood we're talking about, and my childhood was very much defined by Mario 64 and then my early teenage years with Sunshine and then my mid-teens with Galaxy. These games are a part of me and how I started to view video games growing up, I guess. And also they are hard games. It's not just ECPC and walk in the park. They are also hard. What do you think about the difficulty?  Yes, but I will never stop trying to finish the game. Okay, so please check out my cousin's YouTube channel. You just started YouTube. Yeah. What do you think about doing a YouTube channel? I've been very fun. I've never seen so many people in a short time. Yeah, you have gotten a lot of subscribers. Yeah. You're so excited. So go over there and subscribe, link down below. Now, thank you so much for watching today, and we will see you later. Later. Bye!