So yeah, thanks a lot for agreeing to be on the panel. This turns out to be the last event in this workshop. So I think it's a good way to both conclude and look into the future. So let me ask one question first to all the panelists and then we open it up to the floor and to Zoom. So I was interesting to hear your opinion on what you think is the biggest achievement that machine learning has brought to the field in the last few years in your opinion, right? You all work on slightly different stuff. So maybe if you want to start, Zach, and then we go from right to left. Convincing people to learn statistics. I think if nothing else, it has made it cool to learn statistics and alternative propagation and all the things we sort of take for granted. And it was quite hard to convince people to do this a couple of years ago, 10 years ago. But now all of a sudden everyone wants to learn. So I think that's already a big help, even if we call it machine learning. I think the impact of machine learning has been to encourage us to rethink a lot of the problems that we had before in the field where a lot of manual parametrization or intuition has been sort of the norm, like in density functional theory in force fields and from experience what computers have learned to interpret images and text, basically told us that a lot of the manual work can be automated with some of these high dimensional models. And so now all of these approximations can be rethought and redone much more accurately and perhaps automatically. So that sort of encourages a lot of people now to think that all these difficult problems maybe can be solved. Yeah, I mean, together with these two things, I think adding the openness of our data, it's also a very good point. I mean, we are really able to do this thing because we are more transparent in the way we work. And this is going to be a good for all of us. I don't really feel like I'm in a place to say, I feel like I'm more on the other side. But what I think, what I hope to continue to see happening is that there is more sharing of code so that we can have quicker iteration. And I think it's already happening. I definitely don't want to say that machine learning computing has done that. But I think it's happening more and more. And yeah, I think that will be better because we can, you know, that's the only way we can make good progress, I think. Yeah, so I think as experimentalists, it gives us the opportunity to connect simulations and experiments. And we have seen some of that. And I think especially when there is not a direct connection between the experiment and the simulation, machine learning can help to bridge this gap. And this is very helpful. Okay, thanks a lot for this initial assessment. So maybe you take questions from the audience, including Zoom. So just raise your hand. You can ask a specific question to individual panelists or general Andre, for example. Okay, I have two questions. First one, we've seen over this conference that everyone or lots of people are using much more and more complex models like the neural networks we have in more and more layers, more complex architecture. So my thought is, because I'm not machine learners yet, are there a way how we can build something like active learning AI that would architect this machine learning model for you so they would be more efficient by themselves? So this is my first point. And then the second point is, for example, in the catalysis, we've seen lots of new improvements going almost to the close to the accuracy we want. But still all these models are mostly in vacuum. So, but the real chemistry is going in ambient conditions or in water. What do you see how the field will get over this and other dimensionality of having the solution inside and how it will be in the future? Thank you. Let's take them maybe one at a time because they're quite different to questions. So any thoughts from the panelists on using active learning to make models more compact? I think you might be talking about auto ML sort of methods like neural architecture search or other automated methods to find models that work. And I feel like that's already starting to happen a little bit and has especially happened in the image and language side, but I'm not aware of too many great tools for atomistic potentials, but it sort of feels like it has to happen unless everyone in this room becomes an expert at the latest and greatest every single time. And so maybe that's a solution. We all just get really good at it or maybe someone comes up with a clever strategy to find the right representation or make these decisions automatically. I don't know what'll work. Any other thoughts on this point? Rihanna? I think for the image domain it took quite a long time for the auto ML sort of like setting to become maybe still, it's still actually in the process of becoming actually useful. So I think we'll probably hit the point first where we have lots of people who have these magic skills to figure out what's the right architecture and then maybe we'll get there. It's a super hard problem, but it would of course be super useful. Then on the other aspect of complexity, I think there's great opportunity of what you mentioned by fusing computation and experiment in joint machine learning models. Any other thoughts? To me, that's really the only way to go farther if we are able to integrate information that comes from different layers. So not only our traditional DFT or the energies that we can supply from the new potentials, but also try to encode some parts of information of in catalysis, very detailed characterization that we are having and characterization is very expensive. So you want to make the best use of it. So I think that models with multiple layers will be needed in the future. Just maybe to comment on the model complexity and I think it's a very important point that was raised that models are now getting very accurate for a lot of properties, but in practice, they also get slow. So for practical application like molecular dynamics or design the materials, they need to become much faster and sort of consume less computational resources themselves for training and for inference. And so I think the next step in this whole game is specification or reduction of model complexity, whether it can be done automatically or somehow by hand, I don't know. But there's a lot of evidence that we are overdoing a lot of things in gigantic neural networks or maybe very complicated descriptors. And every time you try to simplify things, it still works. So there's a lot of opportunity in sparsifying and simplifying models to become much faster and maybe they'll become also more transferable, who knows. But I think this is where the field probably needs to go. Let's take another question from the floor. Thank you. So my question was already, I mean a little bit talked about in the first talk, but we also produce a lot of data, but I've been feeling we are quite ineffective at sharing it actually with the community. So maybe some thoughts on how you think data and also maybe pre-trained models can be shared the most effective way with the community. So do you want to add anything, Zach? I mean, you're already doing it too. We've certainly been generating data, but I think purposefully, we haven't been working on the problem of how to share the data or include it with others because I look around and it's a monumental task that is very long-term and someone really needs a lot of resources. And in a lot of cases, I think the only way to get everyone to do it is to have it sort of be top-down from funding agencies or someone else saying how to deposit stuff, like what happened with the NIH and the protein data bank and others. It really required the NIH to step in and say everyone has to deposit in order to get everyone on the same page. And just in my own group, I don't see any way that I could do anything on the scale of IOCMBD or Nomad or these other centers. And so I feel like there's already people working on it. It's definitely a hard problem. I feel like it's getting better and hopefully it becomes easier, but I don't think it's something that any one academic group can do. It feels like it has to be larger at an industrial or a national lab or world scale or something. Nasty, do you have any thoughts on sharing experimental data? Yeah, sure. So I think it's incredibly challenging task because there's so many different groups, so many different approaches on how to like working approaches. And you have to come up probably with some sort of powerful ontologies and individual groups should have their own ontologies that are then merged into bigger ones. And so that gives you a layer that can, yeah, in which way you could understand the data from different sources, maybe. And this will be something that the community should work on in the next years probably. Nuriya, you're running a data platform too. Do you have any thought about including models also? Yeah, I mean, a little bit, as both are saying, I think that from the funding mandates that we are all having, this is very difficult to allocate the money to do this thing in a systematic way that will be good for all the community. And the only actual way to do this is either enforce it as a proteins people. Proteins people did it many, many years ago or help us in doing this. Even the data creation, I mean, many of you know that if you have data and you have a European project, you have to project how you will be supporting this data for a little bit more than the extension of the project. But you cannot be covered by the project itself. So this generates lots of problems at different levels, not only the practical problem of fast sharing, but also the practical problem of finding the money to maintain the databases alive. And this has to be syndicated in some way so that we can get our way forward. And I think that researchers need to get recognized by the data that they are putting into databases. Okay, this should be an achievement tool like a manuscript is or a paper is an achievement. There should be and the same goes for code. I mean, when I say data, I also say code. These parts are constituents to what we are doing. And you should get rewarded for this. Okay, let's take a question from Zoom, was it? Yes, this is a question from the chat and it's asking what is your position on machinery models for direct property prediction and whether these can be joined with, for example, machinery potentials. Since after now they have been quite separate entities. Maybe very much depends on which property you're trying to predict. Some properties are very smooth in structure space. For instance, if you're predicting energy, if you're predicting something different, like for instance, a kinetic property or a conductivity of things, very tiny changes in structure will produce very large changes in the property itself. So learning directly could be extremely difficult. And there you need the direct method for predicting the side of electronic transport or thermal or ionic transport, something like that. And that's where, you know, you have machine learning model that accelerates the computation itself that gives you the property. Some properties can just be directly probably mapped from, you know, molecular structure to the output, but it very much depends, I think. So I don't think there's a single answer there. I also want to comment, because if you have seen many of the predictions that we are trying to do, or many of the objectives that we have been saying are related to thermodynamics, and still you have all the burden of the kinetics that we are kind of having in the second layer that we haven't really addressed or not too much in many of the presentations that we have seen this day. Okay, a question from the floor. My question is about how the methods that are developed in this kind of community can be used by non-experts. So real experimentalists and how they, for example, how experimental chemists would, for example, maybe use DFT. And my question specifically is how much you think that the fact that there seems to be a new, better method very often, how that kind of inhibits people adapting our models, so the fact that there's a lot of change, and for example, the leaderboards that we have, and whether that's just kind of in the nature of that this field is still very much developing and we're kind of just waiting for the alpha fold that will blow everything out of the water, and so that it makes it very clear for people to adapt these methods, or how you see that general problem. I think there's more and more interest in how to do really high throughput and distributed inference across really wide spaces, right? We saw one talk today, there was like a billion different crystal structures, that's amazing. I think that's going to get faster and faster. There are things that Google and Facebook and Amazon and others have already sort of set up, right? You don't need to do all that from scratch. Taking a PyTorch model and doing it highly parallel is something that should be possible. And when I think about what has happened on the DFT side, things like the materials project have the same problem, where there's always a new recipe or functional or something, and they don't just have one entry, but it increments over time as they change their settings and updates up. And so it's a hard problem, right? It took a lot of software engineers and others of the materials project to make it happen. But I think that's a really nice demonstration of what we should have for these predictions of these models. I don't think it's going to be experimentalist running models having to learn simulations and everything else. I mean, that's just a lot to ask of someone, right? I mean, that's months or years of effort, and I'm not sure that's the best use of the time, but we should have the outputs of these things be curated and put online so that anyone can see what is the next greatest model, say the energy of some surface or crystal or whatever is in the same way that we just go to the materials project and say, what is stable? Any other thoughts on this? Yeah, I mean, also there is the issue of the cheaper technology gets, the more people gets attracted and the more black pocket it is, the more you can get results that are not really helping you. And this is a danger and this is also something that we need to be aware of that there is a potential to use all these methodologies on many of them because they are far too cheap in the wrong way. But this has happened before and for the rest, for the models, yeah, they will get the point that there will be a consolidation of a standard and maybe you will have changes in this standard, but if you think when the DFT codes were developed, the play with codes were developed, there were many initiatives and at the end of the day nowadays there are a few codes that have survived and I will expect that there will be some kind of reduction over time of all the things that we are exploring. Related to that may be a question from my side. So we've seen in many other presentations sort of reference datasets that came from mostly image processing that helped a lot to get that technology progressed, right? But it seems to me that the material science community doesn't have such benchmark datasets and benchmark cases yet that are maybe understandable to a contributor without too much domain knowledge. So I think what you're building up, Zach, now is the first example of that with the challenges and okay, you have your collaboration with Facebook that helps already, but do you see maybe in your respective fields the emergence of such reference datasets and sort of reference challenges that people can contribute to? So I think there's already others in the community. Anubhav Jain comes to mind as having that map bench where you can go online and they have small data all the way up through materials project scale data for predicting properties and then track models over time and so we go there quite often to see what the best for crystal structure energies and other properties is. The other thing I would mention is the ML community has already addressed some of these challenges with sites like papers with code where the datasets and the models are being tracked over time. I don't think most of the material scientists have done a good job of getting their stuff into papers with code and tracked properly. So when you look at those things are pretty sparse in the leaderboards, but that seems to be the approaches working on them if you have other thoughts. Yeah, I agree. Probably something like that. I think it just has to be a bit more. All the data just has to be shared a little bit better on similar platforms. So I think papers with code is a great way or doing it through these challenges. I mean, how many of you have gone to papers with code before? I mean a couple, right? But I mean these are the things that we should be thinking about, right? Like how do we really get it easy to compare the data sets to the models? But I think it's also quite, I mean, there's so many different tasks, right? You also mentioned, right? That it's quite important to explain to non-domain experts what the task is, right? But out of all the talks we've seen here, if you think about what type of datasets you need to solve all of your problems, that's actually quite a lot of different datasets, right? So I think it's important to think about if we just throw 10,000 datasets at people that are all really small, nobody's going to do it, right?  Now what you see is the potential in sort of experimental data generation for such curated datasets for general machine learning improvement or even challenges, like or similar. Yeah, sure. So I've actually seen a few examples where the code and the data was shared or actually also in our papers we shared the code and data mostly. But there's also efforts by the community to share datasets for benchmarking. These are mostly relatively small datasets, but once you make it public then people could add to it and also refine the code and then something like this could evolve and I think it will evolve in the future. Yeah. Do we have questions from the floor or from Zoom? I think my question is more of a bit more general. So if you think about it at the beginning of deep learning all of the hype and all of the young students who were super excited to do deep learning they were quite motivated by the examples, right? For example, if you have images you can do generate new, I don't know, cat images or whatever. And then this still pub game about which started talking about how does the model work and then a lot of visualizations around what you can tweak and then how things work. So I feel material sciences is as a field so I come from computer science and to me it's quite interesting like the kind of work that's happening. But I also feel this, if I now try to talk to my computer science colleagues it's really hard to explain to them what exactly is going on and I feel there needs to be some sort of, I don't know an outreach program which kind of explains like the kind of interesting problems that we are solving here in terms of like layman's languages. So do you feel there's maybe a gap? I mean, of course now this is completely devoid of what we are doing here like machine learning for materials but then I think there's a need for a general outreach kind of a task that I think we as machine learning for material scientists could do in terms of showing applications of what's happening and then generate more interest among I don't know lay people and then that in combination with the data sets that we just spoke about it will just, I think I feel that's kind of missing and then what are your thoughts and opinions on that? I'll comment on that. You want to solve climate change, you need materials. You want to solve the problems that we are having with plastics we need materials. You need to solve the energy harvesting problem, we need materials. You need to store hydrogen efficiently you need materials. Just a few of them. I think that's great, but that's I think a machine learning person needs slightly more concrete information than we need material. This kind of education I think that is important, right? So if you as a machine learning person you enter a sort of like a new feeling like oh my god this looks so scary I don't understand any of the normal picture I have no idea what you're talking about. It's like okay I'm just going to there's many workshops, right? I don't know if you're interested in in NERBs or in ICML or ICLR for ML for science. But they're pretty as much as we try they're still quite sometimes inaccessible for people with no domain knowledge at all, right? So slightly clear translation into a language that you know machinery practitioners can understand like this is my output, this is my input of my model this is what I want to predict and this is important I think sometimes these things could be improved because for instance if I can I can write maybe a nice paper about a generative model of material and I'll probably you know write something that I think is great and then you'll look at me like that's totally useless what did you do, right? And so I think these type of discussions we should probably have a bit more. Absolutely it was just a a flavor of a few things that if you're interested. I would also go one beyond that I mean getting collaborators interested in your problems is an old problem, right? I mean we've all struggled with this, right? It's not just getting ML people interested in our problems it's getting physicists or mathematicians or statisticians or whoever else and a little bit of it is motivation climate change is a good one and I think that's the best one to hook people with because there's obviously a lot of targets and needs and ESG targets and other things that make these companies very motivated and solve these problems but it also needs to be an interesting problem and it needs to be a hard problem and it needs to be something that they agree is actually hard on their side and cool and fun and pushes the boundary somehow and so my impression for one of the reasons why so many companies are interested in the space right now of all the times that this could have happened is large graph models are quite interesting and hard and are moving very quickly and if we said that the only thing that needed to happen was another image recognition problem to solve climate change there would be some people interested in that but I don't think we would have the same sort of engagement so it's on us I think often to do the translation and find problems that are hard and interesting and sometimes people will just look at you and say that's something I could solve but it's not actually a hard problem and you just have to live with that and that's okay you go and talk to other people or you just download the codes or the reference methods or just do the standard approach but not all the time is there going to be the spark that says the ML people or math people or whatever have to work with you and have to help you on your problem and that's okay Thank you so we're already coming to the end of this panel discussion it's relatively short today but I wanted to ask each one of you one concluding question so we started off with the achievements of machine learning and now I'd like to ask you about what you think is the largest challenge that we're currently still facing just one I mean I know there are many in your specific area that you're working in so let's start at the other end so Lars if you want to Yeah so maybe from the experimental side if you do machine learning you often need input and output values and so in that case it means that you have to have some final conclusion about your results and often an experimental if you look at experimental data this is subject to personal understanding of the data and maybe also the field of the person and so there's not always the one final conclusion which is also maybe an issue with benchmarks and experimental data sets if two people perform a face classification they could come up with different results basically and this might be an issue and a challenge Okay, thank you Rianne Yeah so I think the one thing that I'm worried about is I think a little bit related to what you've talked about also is that we're going to do machine learning on synthetic data only and then for instance we're going to do oh yeah great we managed to fit the DFT functional but actually the DFT functional isn't really that good in the first place right and then I feel like we haven't really done anything useful but I think it's still pretty hard to integrate with experimental data which you probably have because of that It has a big challenge I think training and training for everybody and make sure that we can have careers for different people with different backgrounds that provide different contributions to the chain value in science I mean if we are just thinking about academia we are really missing that we don't have clear career paths for profiles that are mixed we are trying hard but it will be very important that everybody understands the role of different people that it's in a scientific team adding all this complexity because our problems are highly complex and there are many people that can understand and translate them in very different manners I think the biggest challenge probably is formulating problems that can then be solved with machine learning I mean we have techniques in the machine learning world but using them in an intelligent way for predicting properties is some problems are very data driven you start with a structure you try to predict some property but oftentimes these problems can benefit from incorporating a lot of physical priors or exact constraints or symmetries that clearly makes a lot of models much more efficient and more interpretable so the question is how to transform your problem that you are trying to solve I think is describing quantum states density functional theory is not good we can make progress in making it better how do you learn a density functional how do you input into the learning algorithm or in force fields how do you incorporate exact symmetries so that your force fields are not brute force so these kind of problems are connecting what we know in physics and chemistry with machine learning I think is where a lot of work has to be done I think if we want these methods to have an impact the models also have to be robust robust to different structures, robust to different inputs robust to all the different things we ask of them and I feel like in my own group we've developed tons of tools where it's easy to have a demonstration system and the results are good and you are hand published there are 110% of edge cases that crop up and you have to hammer them down and figure it out but it's really hard to do and I think that's going to become more of a problem and I'm coming at this also from a chemical engineering background where there's been this community of people doing systems engineering and optimization and control where if you want to control action for a plant a chemical plant that is processing tons and tons of material that is flammable and destructive and everything will potentially blow the plant up and we don't have quite those same problems with DFT no one is going to die if our DFT simulation goes off course and that's good but robust optimization and robust control and all these other things are very established and I think we need to learn some of these things from other fields because it's really hard to get something that works all the time where someone actually trusts it and says this is as good as a student doing it by hand or agreeing to be on the panel so this concludes the panel discussion now and I hand over to my fellow organizers for the concluding remarks


I want to talk a little bit about another game that you might have actually played. It came out in 1984, not for the NES, but for the Commodore 64 and the Atari computers. It didn't actually make it to the NES in North America until 88. It is called Spy versus Spy. Has anyone played Spy versus Spy? Oh, a lot of people. Wow. Do you remember this game? It's not so forgotten. Can any of you think of a game that's like Spy versus Spy that has come out since Spy versus Spy in 1988? There are very few. So Spy versus Spy for Game Boy. It's interesting you bring that up. That was made. Hey, stop it. It was made by a company you've never heard of called First Star Software. And there's this whole story behind it that's actually not that interesting. They made two games, Spy versus Spy and Boulder Dash. And the company exists today, as far as I can tell, to continue to make money real licensing Spy versus Spy and Boulder Dash. So the deal with this game, those of you who haven't played it, is that it is a direct versus game again. Two players, perfect information. There's nothing to hide. Yet it's hide and seek. You have to get four items, put them in a briefcase, and go out that little door with the airplane out of it. That lets you fly away. And then the building explodes and kills the other Spy. Now, you can only hold one item at a time. And so you get the briefcase. So what ends up happening is you find like the money bag. All right, the other player sees the screen. He sees you got the money bag. This game wouldn't work if you couldn't see each other's screens. Most games, I mean you played Bond as a kid on the N64. And people would yell at you if you looked at other people's screens to see where they were and snipe them. This game, if you didn't look at the other guy's screen, you couldn't win.  So rather than trying to like get around the limitations of the console and the split screen multiplayer, because you couldn't really network to Nintendo's together, they decided to make a game that it was okay to look at the other guy's screen you were expected to. So I gotta hide the money bag somewhere. Like I gotta hide it in the bookshelf. I have to remember where I hid it. I have to remember all the traps I've laid along the way. I have to hope that he doesn't remember where I laid it. And I gotta protect it from him. Yeah, the best idea is to usually put it somewhere and then put a trap in the same room, right? Perhaps you put a water bucket on the door of the room or you put the money bag in the dresser. But then before you, if he tries to go in that room, he'll die. And he probably didn't see, he probably couldn't pay attention long enough to see you set the trap and hide the money bag.   Yeah. The game is not great. It's actually very deeply flawed due to the limitations of the console. Unlike Outlaw, which kind of maximized the potential of the console, this game, you really only have two buttons and it doesn't work that well. Like you have to cycle through all the traps to figure out which one you're going to use. And it's really fiddly. And if you play it on any difficulty other than basic, it's a gigantic map so big I've never seen a human being beat it. Yeah. See, there's only six rooms on the easiest mode. The hardest mode is like a gigantic mansion. It's like, you can't even remember where you left your socks, let alone the money bag in the briefcase. So why don't people make games like this anymore? I mean, why don't people try to make a hide-and-seek versus multiplayer direct competition game? So here's a game that I can't get to go full screen. This game, I doubt any of you have heard of it. So basically in 1984, there was a company called BMB CompuScience. And obviously they were not a game maker. They were a hardware maker. And they made a networking system called ImagineNet. Obviously ImagineNet didn't work out so well. That's why you all use Ethernet. But the idea was that if you were some company and you needed networking in 1984, there really, you know, there were a hundred different choices to choose from. So some people, I guess, might have bought ImagineNet and probably went out of business because of it. It's not a very interesting story. No. But so they wanted to sell ImagineNet to people. They needed some sort of software to demonstrate that used ImagineNet. It was very impressive. So they had this guy, also named David, David Clark, and he made a game that was a network game in 1984. It was called Sopwith.  So I have swapped Sopwith 3 to show you. Now, the game itself is not that amazing, right? Has anyone seen this ever or played it? Wow. Really? It must have had some, it didn't make the run for the internet or something. We'll get this thing going. So here's the deal. It's pretty much a multiplayer LAN versus a choplifter defender. Are you going to play or not? All mixed together in one. Now, a choplifter is a game that really hasn't been remade that much. Defender is a game that... I died. I died again. Oh, there he goes. It's basically a choplifter. The game itself isn't really that exciting. Though it has a surprisingly dedicated fan out. See, one of the annoying things about this game... It's like a choplifter.  One of the annoying things about this game is that when you fly to the left, your controls suddenly flip. So up is down and down is up. And then when you flip, right? But there's also a button to do a barrel roll. And that basically lets you reverse the controls sort of. Wow, the computer's really just gunning for me here. But the magic of this game, right, is that I wanted people to realize this. This game was out in 1984, and you could network play on Imagine It. In 1984, there was LAN play. Mega Man 2 did not exist. Zelda 1 did not exist. Mario Brothers. Five versus five is not even in the US yet. Mario Brothers, maybe around that was 85, right? But there was a LAN game. And if you went to like any college that had, you know, the second version of software, software 2 after Imagine It went out of business, right? When that came out, it actually let you play with regular old networking. So everyone who had just DOS computers in a, you know, LAN in a school computer lab and a college computer lab and a company, they all had software because they all got it for free. It was open source, whatever. But the thing is, right, if you did LAN mode, right, there's a lot of things to be gained from LAN mode that people aren't taking advantage of because they're not even thinking about it anymore, right? The one thing, obviously, is everyone has their own screen and their own set of controls, right? Which, you know, counts for a lot. But also there's that, you know, that feeling that you're in the same room as the other guy. It's one thing to have the Xbox 360 headset.  Because with the webcam, the picture of your genitalia is kind of grainy. It's not like really full feature. Right? When they're... You get the full 3D experience in person. Right? But the real thing is when there's no lab, right? When you're developing a game, when you're programming a game, network code is the hardest thing to write. People don't realize this. And you see a lot of indie games come out and they just mess up the network play completely. It just doesn't work because it's so hard. They don't learn the lessons, you know, from the old, you know, people who've already figured it out, like John Carman. Now, LANs, I mean, people aren't going to go to LAN parties as much as they used to. I don't really expect people to. Gaming laptops are usually not the most economical thing. But what we could do is that a lot of people aren't doing, especially with these iPad apps, these phone apps, is that they don't give you local wireless multiplayer that works. And even with like the DS and the DSi, a lot of games don't take advantage of the fact. You can have 10 people in a room and play a multiplayer game and do something interesting with it. They usually take an existing normal game and tech cons from sort of multiplayer. You could make a game like SOPWIFT, put it on the DSi, and have like the planes flying around the room in 3D. Like everyone's using the camera to look at the space. Imagine if there were like 10 SOPWIFTS against 10 SOPWIFTS. Like, and everyone was flying around and they couldn't, and you were crashing into your friends. Right? That would be a good time, even though the graphics are really sad. They're not that sad. The cows are also at a lot of humor factor. Those are cows? Yeah, that right there is a cow on the bottom, right? That looks like a camel with a gun. Sure.


Hey everyone, welcome back to Nintendo Prime and man we have a really really weird story to talk about today. One that's become all too common on Nintendo Switch. But before we get to that story, I want to announce a special giveaway we have going on for brand new subscribers. So sure, in the month of August we're giving away a $20 eShop gift card to one of our new subscribers in the month of August. But actually from yesterday, so as of August 11th, until we hit 100,000 subscribers, every single new subscriber from then until 100,000 will be automatically entered into a random drawing to win $1,000 cash.  We will have a special giveaway event at 100K for all of our subscribers, not just the new ones, but we are doing a special promotion for our new subscribers, so why not give it a try? Now let's get to the actual news and it's about Hogwarts Legacy. You haven't heard the news yet today, Hogwarts Legacy put out a new tweet and it reads, Hogwarts Legacy will launch on February 10th, so they're giving us that new release date for it 2023 for PlayStation, Xbox and PC. The Nintendo Switch launch date will be revealed soon so I guess we shouldn't have to wait too long for it. The team is excited for you to play, but we need a little more time to deliver the best possible game experience. It is notable, you can actually pre-order a physical edition of Hogwarts Legacy for Nintendo Switch, but we have seen in the past where some companies have offered physical versions that are just the case and it comes with a digital insert inside for you to download either a digital copy of the game or what we're really afraid of, an actual cloud version. A lot of people look at Hogwarts Legacy like you're looking at footage right now and you go, how the hell is that going to run on Switch? And now it's delayed for Switch. Does that mean it is actually coming natively to Switch? Because why would you need to delay a cloud version for Switch? Because it would just be the PC version of the game essentially running on a server on Switch. So I do tend to believe this is probably coming natively to Switch, likely with resolution and FPS drops and everything like we've seen with a lot of other third-party games that come to Switch. I'm still impressed that if this game is coming literally natively to the Switch platform, that is still an impressive feat to me, unless it just comes out and is running at like five frames a second and at like 240p, which could happen. Now, this has been a normal thing to happen on Switch over the years. We've seen a number of third-party games that do come natively to Switch really seem much later than the other versions of the game. Obviously, the Switch version not being prioritized as much as say PlayStation 5, PlayStation 4, Xbox, etc. And in many ways, it's understandable because the Switch is one, a different type of hardware and two, significantly less powerful than these other platforms. So it does need more specialized development time that isn't really getting the full dedicated time it needs during standard development. It's unfortunate that we do have to deal with stuff like this on Switch, but it's pretty much going to stay this way now that PlayStation 5 and the Xbox Series X released. They've been out for a couple of years now, so developers are starting to slowly prioritize development towards next generation, or I guess we should say current generation devices, and that's going to leave the Switch version of games if we do get them kind of in the dust. Again, it's extremely unfortunate, but that's just the way it's going to be probably until we get whatever Nintendo is working on next. And we don't know what they're working on. We've got the rumors and the leaks and the reports, and we don't need to go down that rabbit hole again. I know some people get tired of going down that rabbit hole, but yeah, Nintendo is going to release new hardware someday. That is a thing that's going to happen. Nobody knows when. Next year, 2024, 2025, 2027. I mean, I don't think the Switch platform can last that long, but who knows? Is Nintendo going to refresh the platform instead of releasing a brand new one? Nobody really knows. It's all guesswork at this point. I know Charlotte RGT-85, he actually took the conversation back to the Switch pro route. I know just mentioning that makes some people cringe because he was arguing that it actually still makes sense to release a mid-gen refresh more than the next generation. Most of it has to do with the current momentum of Switch and the Switch sales being strong. And hey, Nintendo keeps saying how they want to extend the life cycle of Switch for as long as possible. And well, a mid-gen refresh Switch would do that. And I know we're technically probably out of the middle of the generation, but still, it could make some sense depending on how big of a leap it is. If it's just something super, super minor, it probably doesn't make any sense. But if it's enough, but things can still be made for the old Switch as well, I don't know. I mean, some people worry about splitting up the player base. You know, like on New 3DS, you could play Xenobit Chronicles 3D for that, 3DS for that, which was a port of the Wii game, but you couldn't play it on the original. I think Minecraft was also only playable on the New 3DS. Maybe I'm wrong on that, but I'm pretty sure it was only playable on the New 3DS. So people worry about splitting up player bases, but honestly, it's better than not getting the games at all. Now again, this is just speculation at this point. What we do know is Hogwarts Legacy is supposed to have its Switch release date announced soon. So with February 10th, 2023 being the target date for other platforms, we just kind of hope the Switch isn't too far behind. Although it could be several months behind, it's just something we've grown accustomed to unfortunately. But again, we don't know if it's going to be a cloud version, if it's going to be local, digital only. There is a physical version that you can pre-order, but again, we've seen physical pre-orders in the past that were just boxes with digital codes, and I think that's a really, really crappy and shady thing to do. But companies do that.  But for those that don't pay attention to the entire news line and they just walked into their local GameStop and pre-ordered a physical copy for Switch and all of a sudden the digital code, they might be a little bit upset at that. But hey, I don't know, we're all just guessing at this point. And this is just the latest, like Gollum got delayed and then now the Switch version is extra delayed on top of that. So we've seen this with Gollum. We're going to see this with a lot of games. I mean, I am glad that we're still getting third party games on Nintendo Switch because I did think the deeper we got into the PlayStation 5 and Xbox Series generation that we were going to see less and less third party games coming to Switch, so far that hasn't actually been true, but a lot of companies haven't moved on from PlayStation 4 and Xbox One yet. So we might still get another year of getting third party games just because those older platforms are still seeing a lot of support. But when Switch does get proper attention, we do know games perform well. Lego Star Wars Skywalker Saga was actually best selling on Switch in a number of territories and came out day in date and ran very well on Switch, at least well for a Switch. Obviously all the other platforms it ran better on, but it was an acceptable experience on Switch enough so that many, many people, including yours truly over here, really enjoyed it. Now that's what we got for you today. The code for our other giveaway we have going on, if you don't know what it is, it is in the down in the description in the pinned comment to get an extra 20 entries, to get an extra 20 entries. That code is Green Bay. Yes, I'm watching a little bit of that Packard game tonight, so Green Bay is the code for today. Alright guys, thank you so much for tuning in and I'll catch each and every one of you in the next video.


Facebook algorithm, how does it work in terms of, let's say you liked my posts more often, does that mean I will see your posts more often as well? If you like my stuff, you comment on my stuff, does the Facebook algorithm somehow say, well, that means we're gonna show George your stuff too? No, it doesn't work like that because imagine if it did work like that, then everyone could just manipulate the algorithm and just going to the people they want to be seen by and goes like, comment, comment, comment, comment. Now, you might say there is an indirect effect, which is if you like my stuff and comment on my stuff often, liking my stuff, the bigger someone's engagement is. So like right now I'm grateful that when I make a post, I get enough likes where I don't even notice who exactly are the ones liking anymore, but I certainly notice the comments, right? I'm still not big enough where I don't notice the comments. I do notice the comments. And if someone's comment to me is thoughtful or interesting, it adds something to the conversation, then I might click on that person's profile to go and see who they are, or even if I already know them, I might go click on and see what they're up to lately, right? And the action of me landing on your profile, excuse me here, sorry, I had to clear my throat. The action of me landing on your profile makes, does affect the algorithm because Facebook says, hey, for whatever reason, George visited so-and-so's profile, so we're going to show, we're gonna basically it's like bumping up your profile, a few points or bumping up your posts, a few points in the algorithm so that when I'm surfing Facebook, I'm a little bit more likely to see your stuff. Now, it's even more likely if I actually interact with a post on your profile, then obviously it makes sense. So the algorithm is always trying to figure, the algorithm just to be clear is always, it's very clever.  It's an algorithm between George and Stephanie, between Eric and Gregory, between Donna and Grace. There's an algorithm for every single relationship you have on Facebook. And so the more you interact with somebody in any way, including private messaging, now you might say, well, George, so can I just keep private messaging you and then you'll see my posts? No, because if I don't respond to you, right? But if I'm responding to your private messages, then it bumps up the points more because Facebook goes, okay, there's a private conversation going on. So George might be interested in this person's posts as well. So the algorithm is always trying to figure out what's going to make, if I'm surfing Facebook, the algorithm is trying to figure out what's gonna make George happy that he came to Facebook today. To say, what's gonna make George wanna keep coming back as Facebook is showing stuff that George wants? Well, how does Facebook know what I want, except from my interactions? So that's how that works and I hope this is helpful. If you have any other tips for the Facebook algorithm, you can go and comment below.


United States. I've enjoyed being your senator for these last six years. There are no finer people in the world to serve than the people of New Mexico. The Senate on Tuesday. How about it? Will you help me? Thank you. Thank you. Oh, New Mexicans, it's my great privilege and my honor for the second time this year to introduce to you a wise man. A man of great courage and of good humor, as we have all seen as he struggles through and the nation struggles through the assassination attempt only a few months ago. A man of great warmth and compassion. A man who is leading us away from the tax and tax and spend and spend policies of the past. A man who has placed the taxpayer of America first. A man who has placed America first in the world. A man who has come to office and come to us today at a time when American need him most. Ladies and gentlemen, fellow New Mexicans, the President of the United States, the Honorable Ronald Reagan. Thank you very much. Thank you. Thank you very much. Thank you very much and I thank the senator for his very kind words. The governor for his... I may be a little previous with that, but I'm sure... for his words of greeting and in all the things that Senator Domenici said about the state here, he kind of left out the fact that you must be kind of a crosswords. Because someone just held up a sign over there, ex-Iowans, greeting me. When I got out of school, I became a sports announcer in Iowa and I have just found out that here is a lady that was in first grade in Galesburg, Illinois when I started to school. So, well, I am happy to be here on behalf of New Mexico's Republican Party and all of its fine candidates for office. Let me pay special thanks to Corky Morris and all the campaign workers for the Republican results that we need next Tuesday. It feels good to be here in the land of enchantment and far away from a place of disenchantment on the banks of the Potomac. Jack, are you sure you want to go back there? Of course, having once been an astronaut, Jack Schmidt is probably the only one who feels at home there. Because Washington is in orbit almost to the time about one thing or another. You know, when he was first elected to the Senate, he probably thought that, like L.E.T., he had landed on another planet. He was one of the few among those alien big spenders, big taxers who was working to bring economic order to our nation. If the entire Congress had voted, like Jack, we wouldn't be in a recession now. Think of it, if only half the members of the Congress had voted with his integrity and foresight, there wouldn't be 11 million people in our country unemployed. There wouldn't have been the double-digit inflation or crippling interest rates that caused recession, and there would have been prosperity. It seems to me that a man with that kind of perceptiveness is the kind that you want in the United States Senate.  Now, Jack is one of the senators who brought logic, common sense, and strong character the task of making government work again. He has shown the grit and the honesty it takes to make the hard choices to cast the tough votes. As I came here, I came here to tell the people of New Mexico I need Jack Schmidt in the Senate, and I need Manuel Lujan, Joe Skeen, and Marjorie Bell Chambers in the House just as much. And let me add a word about John Irick. As you know, we're working on something back there called federalism, in which we intend to restore the tenth article of the Constitution, which means giving back to the states, to the local communities, and to the people the powers that have been seized over the years by the federal government. And to do that, you need someone in charge here who knows about accepting those responsibilities. And incidentally, our plan is not one of dumping those responsibilities on the states. We intend to also provide the means to support them that have also been taken and usurped by the federal government. So I know that I can count on John Irick as an individual, just as the people of New Mexico can count on him. But with Jack's, Manuel's, and Pete Domenici's, and Joe's help, we've been cleaning away the economic wreckage that was dumped in our laps. Now, of course, you'd never believe that if you listened to our critics. They say this economy is on its knees. But if that's true, that's quite an improvement, because two years ago it was flat on its back. But I realize there is still hardship among our people. And if I thought that we were seeking, what we were seeking to accomplish would be only temporary, I wouldn't ask for the American people's patience. But for the first time in our post-war history, we're trying to build a recovery that will last, not just a quick fix to see us through the next election. There have been seven previous recessions since World War II. All of those, in the 26 years in which the Democrats controlled the Congress, both houses, all of them have been treated with a quick fix. Yes, temporarily they seemed to bring an answer, and then a couple of years later there was another recession. When we took office in 81, the American economy was being cannibalized. It was being devoured by runaway federal taxes, federal spending, and federal borrowing. And the result was double-digit inflation for two years in a row, high unemployment, a 21.5% interest rate, the highest in more than a century, nearly a trillion-dollar debt, the largest peacetime tax burden the Americans had ever known, and government spending shooting up at an annual rate of 17% a year. And still, the big spenders wonder why the American people want, what a stubborn minority in the House of Representatives denied them a constitutional amendment to balance the budget. Well, they're going to get another chance to vote on that because we're just going to keep pounding on the door with it until we finally get it. You know, from our very first day there, we've been working to undo the economic mistakes of the past. When I finished taking the oath of office on that January inaugural day, the first thing I did was go right back into the Capitol Building. I didn't even take off my top coat. I sat down and signed a memorandum freezing the size of the federal government that it would not increase in size.  I went there to face up to our problems and with the support and the prayers from wonderful Americans like yourselves, we are beginning to solve them. Now, I don't pretend that in 21 months we've been able to undo the damage of more than 20 years. 21 months ago, we faced five immediate problems. Runaway spending, double-digit inflation, catastrophic interest rates, the highest peacetime tax burden ever, and high unemployment. We've made dramatic gains on four of those five problems. The inflation rate that reached a high of 18% in January of 1980 has been cut to 4.8% for the first nine months of this 1982. And you know, there's something interesting about that 4.8% figure. The last time inflation was down to 4.8% was when the last Republican president was leaving office, Jerry Ford. The prime interest rate that was 21.5%, the month I was sworn in is now down to 12%, and we're not finished with it yet. We've cut the growth in federal spending by nearly 2 thirds, from 17 down to 6%, and we will have cut your tax rates by the first of next July by 25%. Now, that leaves unemployment, which is much too high. In homes across this country, including a home at 1600 Pennsylvania Avenue, unemployment is the problem on everyone's mind. Getting Americans back to work is an urgent priority for all of us. And other than keeping the peace, I have no higher priority. Just as surely as lower inflation has started the interest rates tumbling, lower interest rates will reduce unemployment. Unfortunately, unemployment is always just about the last to feel a recovery. It has been in all the other seven recessions. The interest rates have to come down. People have to be able to afford mortgages again and installment payments and cars and so forth, and then as that takes place, more and more people go back to work. Well, the big difference now is we're going to do it right. We're building a lasting inflation-proof recovery instead of a quick political fix. And make no mistake about it, America's future is a future of hope, and it grows brighter each day as more people respond to the drop at inflation tax rates and interest rates. That's where the trends are pointing. Just today in the East, I don't know whether the news has reached here yet, but it was announced this morning in the East that again, the economic indicators that indicate whether we're dipping deeper or whether we're coming up out of it for the fifth out of six months was up continuing the climb toward prosperity. And also interesting enough, that one out of six months, which was August, when they said that the indicators were down nine-tenths of one percent, they corrected that this morning and said, oh, they were only down two-tenths of one percent in August. So it was a pretty-it's been a pretty good show for the whole six months. But look at the other indicators that the opponents of these candidates are denying or refusing to recognize. Real wages are up. Retail sales are up. Housing starts are up. The value of the dollar is up higher than it has been in the last ten years. It's the highest priced money in the world. The stock market is up. About 220-odd points in the last ten weeks. Research and development spending is up. Business starts are near a record. Personal savings rate are at a six-year high. And new IRA accounts are pumping some 30 to 35 billion dollars into savings and investments that will be able to help provide jobs. Confidence is returning to America. This great nation of ours is on the move again. And I say to our opponents, if they aren't ready to get on board and help, then tell them to get out of the way. But all we can read in what they say is they want us to return to the policies of the past. Well, I don't think the American people want to return to the policies of failure. The policies of the past have burned to the ground. They've failed. And those who would turn America back will offer except more of the same old programs that got us into this mess. So I say, let's stay the course and finish the job. We've worked for a better alternative. An alternative that offers hope for the future and is already making real progress. We're on a new road now. A road that's leading America to better times. And I want you to know that we intend to stick with us and turn this economy around. In Illinois the other day, I met a young lady named Stacy Hodgkins. Now, don't jump to conclusions. Stacy is only in the sixth grade. And Stacy had just won a statewide essay contest. And she was pretty imaginative because her essay was a letter to our country. And it started, Dear America, I just thought I should write you a letter to let you know how great I think you are. I'm not going to read the entire essay, but she closed her letter to America, saying, it really doesn't matter whether we're black or white, atheists or Christian, your doors of opportunity are always open. So knock, knock, America, here I come. Well, Stacy Hodgkins and all our young people, they're what this election is all about. What kind of an America are we going to leave them? I pray that I will see the day when we will have made some payments to reduce that trillion dollar debt that is hanging over that young generation and that they will know that at least we tried and we didn't want to leave it all to them. Now, when you go into that voting booth next Tuesday, vote for your children. Vote for the men and women who are looking to the future and to your children's future, not to those who looked to the failed policies of the past. Jack Schmidt is a man who understands the future.  Jack, along with all of these fine candidates, John Iric for Governor, Manuel Lujan, Joe Skeen, Marjorie Bell Chambers, all of them in the House, all of them will bring to government the responsible principles that will serve future generations. Together, we can make our country the source of all the dreams and opportunities that we wish for our children. And it's only fair enough because some generations that preceded ours left us a pretty good deal to take over. Now, we can make America what she was placed on this good earth to provide. And I can't close here in this place of all without saying something else that with all of these economic problems we faced and that we're trying to solve, I was asked a question many times during the campaign of 1980 because I had said that it was going to be necessary to once again restore the defensive capability of this country, our security in a very dangerous world. And I would be asked the question, well, if it comes to a question of balancing the budget or rebuilding our defenses, which will you choose? And every time I said without fail, I would have to come down on the side of the defenses of this country because that is the prime responsibility of this nation. And you know, with all those uniform caps that I saw waved there. I want to tell you something about this all volunteer service of ours. There were people that were pessimistic and said that it couldn't work, that we would have to use compulsion. Well, it's working. And the morale, the quality of your men and women in uniform should make every one of you proud. And that's not because we're warlike. That's because more than anything else, we want peace. And with them, we have a far better chance of keeping the peace than when we were looking helpless and unilaterally disarmed in the eyes of the world.  And it isn't meant to sound warlike because, as I say, I believe that it is in this way. It's this that has brought the Soviet Union into negotiations with two of our negotiating teams in Geneva, Switzerland, to try and reduce down and hopefully to zero the number of strategic weapons, nuclear weapons in the world. But I've received a letter from our ambassador to Luxembourg one day. And he said that he'd been up on the East German frontier and visiting the Second Armored Cavalry Regiment. And he said a young trooper followed him over to his helicopter and asked him if he thought he could get a message to me. And the ambassador allowed us how he could. And the young states are up here to the statewide jobs they're seeking to the House of Representatives and please don't send a senator up there who would cancel out Pete Domenici's vote. You send Jack Smith. So much military out there. I never intended to say anything about it. No, that's great. Thank you so much. That was great. Thank you all. Thank you, Mr. President. Stay in place until the plane gets going. We can cheer all we want, but we're supposed to kind of stay where we're at until Air Force One leaves.


Hey guys, welcome to Higan Iruthil, and we have a new teaser here. Shero Snowhoney, obviously she's Hydro. I'm just gonna confirm this. She is an SSR tank. But my question is, is she a must pull? We'll be discussing it in a while. So let's go over a couple of data. Oops, there you go. This is the data that I was referring to. So we have here Shero as a 3 star Hydro, cutie gargant, whose unique deflection. When we say deflection, there are actually no skills here in this tier. This tier list was a tier list for the beta test. I remember Shero deflection. So take note, if she has deflection, it will bounce off her and actually hit probably damage dissipation, probably part damage reduction. I'm not sure. I wasn't able to get her, but definitely as confirmed, she is going to be an SSR tank with Hydro element. So what is actually the implication that we will be getting? Shero, I just hope that they give us time to save for these banners so that we could pull for Shero. But at this point, we don't have much Hydro players that we have or not players, Hydro units. So the most significant is going to be Basil, which is a hoplite. Next one is Liv, which is a caster. We have a healer. We have Celia as a healer, but we don't usually use Celia. So the only thing that she's going to have an implication on is on encroaching cold build. So with that, there are a couple of builds already that I'm thinking involving Issa, involving Basil, and involving Liv at some point. So we might have a new encroaching cold team soon as we have her, but my question is, is she a must pull? At this point, I'm going to say no because I feel that encroaching cold, if you want to build this team, you're going to need a healer better than Celia. So that is actually my thought. You're just going to need somebody better if it's going to be a pure encroaching cold team. So this team is, even if with her, with Sheru, this lacks one element, which is healing. You have here a caster, you have here a hoplite. Then she is a guardian. Definitely you will need an Aduitrix, a more solid Aduitrix than Celia. So you have Issa here. Again, you have a caster. No, no, no. Sorry. Liv is an Aduitrix. So Liv can provide healing, but she kind of lacks that bulk of healing. So I wouldn't rely on her for a lot of healing, partly partial healing, but sorry, I'm just going to redo what I said. So we have Liv as an Aduitrix. We have Basil here. And actually Black is also good. So you have, at this point, you have a complete team. You have a caster here. Issa, even though she's an R rated hero, she is going to be good. So that is my, those are my thoughts. So again, they're a bit complete. Just want to, you know, if you want to supplement healing, you can always pull in other healers and other elements. But if you want to go with a pure encroaching cold team, you can do it actually, but you just have to live with Liv, who is a partial healer, not a full healer. She has a lot of support skills, but she isn't really your, you know, your go to healer at this point. Probably your healer at this time. But again, you would have Sheru, Basil, probably right beside. You could have Black at the back and you could have Liv at the back as well. You could have Issa if you, you know, Issa is going to be your, you know, your manipulator in terms of formation, drawing them in the middle, then pouncing on them. This is going to be, you know, this is going to be more of a manual play than your auto play. So again, if do you think that Sheru is going to be needed at this point, is she a must pull? Not at this point, I think encroaching cold needs more units. But again, same situation with Nunu. With Nunu as well. We don't have a lot of animal units, but again, she is actually helpful even if she is not in a pure animal team. I think as well with Sheru, I think she would work well, not necessarily as with a pure hydro team, but she can work with other teams as well. We just have to see her kit and probably review it once it comes along. So hopefully we could have a peek by next week on how she is built and probably review her kit again if I think that she is worth your pull. But at this point, first glance, with Gildan, you also have Gildan, you have Caesar and a lot of serviceable, you know, guardians that mix and match with other elements. I don't think she's a must pull at this point, but she will be key in you creating that encroaching cold lineup. So that is it guys, thank you very much. So again, put your comment in the comment section below and let's discuss how is Sheru and would you pull for Sheru based on that information or would you need more information. So thank you very much for staying this far. Take care, stay safe, this is The Warden and I'm out of here.


Oh, my, my. Okay. Hello, YouTube. It's Sunday. We are resting and we are discovering things.  As you know, true love and I love books. We love books. It runs in the family. You, as you know, I love books and I love the New York Yankees. I usually am allowed to order three or four, maybe five books every Christmas. Yeah? Christmas is coming up, so I was just thinking, okay, what is going to be my Yankee book for 2018 that I read? Well, one, Chasing the Dream by about Joe Torrey. Two, Yogi Berra. Three, the Phil Rizzuto story. Four, Yogi Berra again. Wait for it. Wait for it. Okay, I think I, I think I read this book. I did read this book. I have at least eight books about the New York Yankees that I have not read yet. No, I do not need to order any more Yankee books. We have so many books. We have books hidden behind books. Does anybody else out there struggle with this? Damien? Damien? Anyone? Anyone? We have books inside books. Okay. Love you all. In conclusion, open your eyes to what you have right in front of you. You never know what you might find. Everyone in the house is a little sick. So, but at least the sun came out. The sun still rises. Joseph, Seth, Mama. Everyone is just a little under the weather. I came outside because it's a little hard to watch the Broncos right now. They're struggling. There's three and six on the season. This is my eighth day off from running. Not too shabby. Eight days in a row. My legs are feeling awesome. They really are, you know. They still feel strong though. So, I don't feel like I've lost much in eight days. And I'm gonna start running again tomorrow. One mile. So, I'm definitely getting a little antsy. You know what I mean? So, my running stuff set up.  If you want a long, if you want a long running career, it just, it doesn't hurt to just take a good week off. At least a week. Your body just really needs it. So, that's what I did in the last week. And just get my sunglasses out. My jacket. So, it all starts tomorrow morning. Right and early. Maybe regretting this in two hours. I don't know. I don't know. First time ever. Should I do something? Are you ready for me to look? Look, look around. Might be a little bit of a risk. Right, you are. A little bit of a risk taker here, but I'm going for it. Fung beds. Sharon. This is the whole situation. I'm gonna back up so you can see the whole situation here. When the baby comes, you can sleep in the lobby. Yeah. Don't eat those anymore. He's shaking his head. Yes. He's shaking his head. Yes, Joseph. Oh, that was precious. All right. Here we go. Closing the doors. Sleep with the angels. Rise with the saints. Good night. Good night. Good night construction site. Howdy, brothers.


We're talking about lives. We're talking about minds. We're talking about, ideally, as Randy Bass puts it, the whole person. And the whole person is much more than a set of accessories bolted on to a body. It's about a life lived in space and time with the potential to touch many other lives. Thanks for joining us today, Gardner. It's my pleasure. Appreciate you coming in. How do you define personalized learning? I'd like to tell you a story about personalized learning. I took a creative writing class at Wake Forest University when I was a junior. The creative writing class was taught by a professor I had had three or four times before that. So I was very eager to be in the class with her. Really loved her approach to literature. Was really eager to see what she'd have to say about writing creatively, not just analyzing the poetry. The first day she came in with a collection of newspaper articles that were going to be prompts we could use for our first writing assignment. One of them had to do with a brain behavior relation story. And I said, I'll take that one. And she smiled. And she said, I thought you might. That's personalized learning because she knew me as a person over time in all the variety, all the ignorance, all the curiosity, all the eagerness that I brought to the learning situation. It was personalized because I counted as a person in the eyes of another person whose expertise would guide me through a new learning experience. The problem with that kind of personalized learning, obviously, is that it doesn't scale. Although it might, we have civilization, we keep trying as human beings to get those personal contexts to pervade our relations with each other. I like to think there might be ways to make those kinds of personal encounters scale, especially if we look at networks not just as tools or information distribution systems, but opportunities for connection. People are trying to make college more accessible, which means more students, which means more overload of faculty. At the same time, budgets are being cut. How do you make these gears grind together? It's a great question. I fully believe it is our obligation to make the experience I've just described as widely available across socioeconomic class, underrepresented groups, as possible. All people deserve that kind of human contact. Many accessibility strategies focus on certain kinds of scaling that, to me, has a cruel irony attached. The irony is what we eventually make available is a very thin version of what it was that we wanted to share. So if 100 people have access to a thin experience, I don't know that that's a great gain over 10 people having access to the real thing. The bottom line for me is we have to figure out a way for 100 people to have access to that thick, rich experience. One of the ways we can do that is to empower the individuals to scale their own meetings on the network. Meeting is kind of a terrible dread word for a lot of us in administration, because it means, as Ken Robinson says, our bodies will take our heads to another place and we will sit there. But there's another way that we can describe meeting. Dietrich Bonhoeffer talks about meeting as one of the spaces in which humans encounter each other in particularly rich and meaningful ways. But to do that, those have to be meetings that emerge from the individual and a desire for connection. It turns out the web was built to facilitate that kind of connection. My question is, is higher ed ready to tap in in very meaningful and deep ways to students' dispositions to connect? Can we guide and grow those dispositions, empower students to be aware of the possibilities of connections in ways that are far beyond complying with requirements and getting a degree? That, it seems to me, scales across multiple individuals.  Well, if we look at the sort of hype that's around personalized learning right now, what do you think that's bubbling up that's new that have people talking about this? Because, personally, learning, I guess, in one form and others, been around since you were a child. It's an excellent point. Personalized learning is one of the things that schools were built to facilitate in the sense that I described earlier, a real sense of connection between the expert learner and the novice learner. Why getting so much interest now, or I guess another way to put it is, why is personalized learning buzzing? I think it's because, and I hate to say this, but I really do have a strong suspicion, we are now learning how to work with personally generated information at scale in ways that lead us to believe that we can use the information or the data people produce to design personalization for them. And then we get into a weird metaphor, which is we can fit your feet exactly because we can measure your feet better than ever before. Well, there's value in that, as anybody who wants a nice fitting pair of shoes will testify, but we're not talking about shoes. We're not talking about people and clothing. We're talking about lives. We're talking about minds. We're talking about ideally, as Randy Bass puts it, the whole person. And the whole person is much more than a set of accessories bolted on to a body. It's about a life lived in space and time with the potential to touch many other lives. I wish that latter part was the real energy behind personalized learning as it's buzzing today. I fear that it isn't. I wonder if it's too late to bring the whole person back to the conversation about personalized learning. Maybe it isn't. Maybe there's a way to bring the learners themselves into this conversation in ways that are not just about granular analytics, but about building mindfulness and the disposition towards reflection more deeply, not just into particular classes or an e-portfolio, but into the experience of curriculum itself. I've been working on this very hard at VCU with a number of really great colleagues. It's a challenge. People really do shape curriculum in ways that they believe will conduce to the education of the whole person. But given the way schools are put together and the way we've managed certain kinds of scaling, curriculum ends up being stacked like bricks, pieced together like Legos. And students treat it as just a stack of stuff because that's the signal we send. Can we think about personalized learning with regard to curriculum as a support for those learner-initiated connections? That's a very interesting question. I think it plays in very well with what Randy Bass talks about when he describes the digital ecosystem. It's not just the world we live in as the world we help to make. How do we bring that knowledge to students over the course of a curriculum, over the course of a degree? That's an interesting question. Analytics, great, useful tool, bad word. Well, again, it depends on what we're talking about. Student success and personalized learning. Golly.  You know, one of my most charming qualities is that I will rise to any bait. It really doesn't matter. I don't care what the lure is. I don't care what it's on the hook. It's like, oh, bait. Brian Alexander will tell you that as well. So, let's see. Analytics, certainly we want to be able to generate meaningful data and data are not meaningful on their own. So that's the first thing I wanna say. But we wanna be able to generate meaningful data to help us understand things in ways that will enhance our ability to predict, enhance our ability to meet needs. We've learned this from cellular biology. You can look at my arm and never figure out what's wrong, but if you have an X-ray machine or you have a theory of cells or germs, these are very useful tools. They're analytics. But what about analytics with regard to the whole person idea of human learning? Randy Bass to use him, I just came from Randy Bass's plenary. It was incredibly inspiring. So I've got Randy Bass on the brain, which is a nice thing. I like that. I like it when he lives in there for a while. Randy Bass a couple of years ago talked about this idea he called slow analytics. And I don't think he's really built that idea out a lot, although it's implicit in some of his work now. But the idea was like the slow food movement where we weren't just trying to get it over with, analytics would similarly have a useful extension through time that would help us understand not just what the signals were in a particular course or even a particular degree program. But I think what he meant, you can ask him, raised the learner into an awareness of the analytics they can imagine and perform for themselves. Now learning theorists have had a word for this for a long time, they call it meta cognition, thinking about thinking. If there are analytics and analytical frameworks that can help the institution meet the needs of students, but also help them meet the greatest need of all, which is for students to have a more sophisticated understanding of their own needs and identities as learners. I'm on board with that, I think that's great. And I think we can empower students to continue to track their own learning throughout their lives. Recognizing opportunities not just to find a job but make a job. Recognize opportunities not just to be shaped by culture but an opportunity indeed an obligation to help shape and build culture themselves as agents, as empowered learners. If what we do in our institutions build out in our own practices and empower our students as a result of those practices to shape meaningful analytics for themselves, for their entire lives, I think that would be great. I have to be honest that most of the talk about analytics right now doesn't seem to rise to that level of aspiration. And I think the danger is bricks are useful, but if that's the metaphor with which you build your world, you may not end up with anything but a pile of bricks. I also wanna stress that there is an ethical obligation that we have to students to help them understand what data we collect, why we collect it and that in the end, it's their data. They need to make informed decisions about how and when to share that. Not just in the context of FERPA, let's not share anything, we'll take care of that.  We have protected you. As Michael Feldstein pointed out in his recent article in the Chronicle, students should understand the benefits of sharing data about their learning and how that story can be told in ways that will benefit them beyond any single course or degree program. We have this thing at VCU we talk about that's called generalizable education. Education with impact beyond any single course or degree program or major. And the idea is you will have this experience, it will help you shape the shaping for the rest of your life. Analytics, the generation of data around activity certainly has some part to play in that, but not in a custodial arrangement on behalf of the institution, especially not on behalf of the institutions claims towards prestige, but instead as a way to help students understand how we're trying to understand them. So that can lift them into a higher sphere of self understanding. There's a mindfulness you have, there's a society you see in your head that 90% of the population doesn't have time to think about, doesn't have time to envision, doesn't even have time to participate in. They're busy eating at McDonald's while you're busy trying to make slow food. And that's got to be a frustrating place to be. The things that you talk about in terms of envisioning the whole student as a mind that's more than a set of bricks for courses, you've got a big wave against you because accessibility to education is not looked at that way. It's how can we piecemeal it out to as many people as possible so they can check that box and say, I have a college degree. What do you do with that kind of frustration? I struggle with a good way to describe the struggle. So as is always the case, I'm always going for the meta thing. It is a necessary set of ideals, principles, mindfulness, I think, to advocate for. These are ideas and principles and the kind of mindfulness that I try to practice not because I thought it up, but because a great cloud of witnesses throughout my life, including most emphatically my own best teachers, showed me what that would be like. I am the product of educators who care deeply about the kinds of things I try to talk about and live. And in many instances, those beloved professors found ways to do this despite enormous odds, having to do with their own sexuality or their gender when women weren't in the academy in great numbers or the unpopular opinions that they would espouse or their stubbornness in their English department meetings. People kept the faith for decades preparing for the moment in which I would be able to be empowered and enlivened by their presence. I am sure they got frustrated just the way I get frustrated. I am sure that they had days in which they thought, you know, maybe I just go need to find an honest way to make a living because this just doesn't seem to be it, but they kept the faith. And I feel like if I didn't keep trying in my own ways to do that, I would have betrayed the gift they gave me. I may not do it the right way all the time. Probably I don't. I may not do it with the success that they had. But these are not principles I made up. They are not idiosyncratic. They may be difficult ones to think about at scale. Good, that's a challenge. Let's get going. We're human beings. We eat problems for breakfast. Let's go. They may be difficult to fold in to the busy administration of a very large university. Okay, I get that. Lots of tasks, we'll get them done. But task number one is, how do we look at the people who empowered us to be here today and keep that standard aloft? Keep that flag waving. Do something honest and heartfelt and smart to honor what they gave us as we got here. And then in turn, make that more available to more people across all social lines for the future. That's the future I want to build. Do I get frustrated? Absolutely. Absolutely I get frustrated. I get frustrated when people talk about this stuff as, oh, that's the 30,000 foot level. What are we going to do about this, that and the other thing? I get frustrated for two reasons. I don't think it's the 30,000 foot level. I think it's the foundation on which we build. But I'll be honest, I also get frustrated because I understand how complex it is. And I understand that a lot of people of goodwill are really trying to build a better higher education for the future and for more people. And I get frustrated because of the enormity of the task. I just feel overwhelmed. And I know that others around me do too. But when I see great colleagues doing amazing work that inspires me, I feel like, well, you know, in the grand scheme of things, I won't quote Casablanca right, but the problems of my little world don't amount to a hill of beans in terms of what it is that we're trying to build together. And I feel that in my work, I want to honor and support those whose work has been so vital for me. But I'll end that with another little story. A story about one of the things that really keeps me going. Monday night, about 36 hours ago, I guess or so, I was in a room with 24 young learners who had gathered together to take a course called Milton. I'm the instructor for that course. Milton is my disciplinary specialty. We sat in that room together. I asked them, all right, it's May. What's the happy ending? What is it that you hope will have happened as a result of this experience? A couple of them spoke of grades. Most of them did not. Most of them had within them an idealism and a hope and a courage for what they wanted to do that far outstripped this idea of bricks or Legos laid end to end. That's what keeps me going. That's why I, you know, never give up, never surrender because there is still hope most conspicuously in those learners that this will be something greater than the sum of its parts. In a Milton class. All right.


Ar gweithio a'r tvr yn y Ffnith yn ddewis. That speculation is a mountain that Evernton is ready to announce Raphael Benitez as the new manager. That is what a lot of the pressure hinting at today. Apparently Greg Ockiff was on the radio show before the athletic journalist said that Evernton, he expects Evernton to announce the appointments of Benitez imminently. It kind of goes in line with what other stories are being out this week about Evernton appointment Benitez this week. Obviously at the moment that hasn't been confirmed that Nuno Espirito Santo is still in with a chance, but it does look increasingly likely that it's going to be Raphael Benitez as the new manager show. The club, if that is the route they take, I've obviously seen that a lot of people are not happy with the decision to certainly speak to him and to a point of him as the manager and it seems like they've made the decision no matter what. So it's a brave decision by the owners, if that's the right word for it.  If that is it and he is confirmed in the next 24 hours or so, then Evan will have to get on with getting this playing squad sorted out and bringing in new players because the squad needs players, regardless of who the new manager is going to be, the squad needs players in ASAP. So yeah, that's kind of what it's pointing to at the moment. It could change of course until the contract is signed, anything could happen, but that seems to be way ahead at the moment. There has been reports today, Lincoln hammers Rodríguez with a move to AC Milan. Apparently the report coming out of Columbia but also out of Italy today Calcio Mercato reporting that George Mendes, Rodríguez's agent or adviser, wants to move him to Milan and wants to move Raphael Lau, the other way to Everton. I think a lot of people thought once Carlo Montiolotti went, I mean I'd seen a lot of people before Carlo went saying hammers wouldn't be back for Evan next season, but a lot of people thought once Carlo Montiolotti left that hammers Rodríguez wouldn't be too far behind them. We've had a couple of stories linking him with a move to Ryan Madrid, linking him with a move to Athletic Home Madrid. This one saying AC Milan, this one seems to have a little bit of a plan behind it, the kind of thing Mendes does. From an Everton perspective if he was to go and Raphael Lau could come into Everton as part of it, I think that would be a good move for Everton Lau, can play anywhere across the front three, got goals in him, young, quick, strong, wouldn't it be a very good move for Everton to be honest with you. Especially if hammers doesn't want to be here, but I thought it was interesting that Calcio Mercato reported it as well today, interesting to see how that plays out. Finally reports coming out of Portugal today saying that Mateus Nunes has moved from Sport and Lisbon to Everton, is on hold at the moment, while Everton sought out their managerial situation. They do report that the 18 million euro deal is in place, has been agreed between the two clubs, Marcel Brands has agreed it, the club now just waiting for the OK off the new managerial proceeding with the deal for the 22-year-old Brazilian warm of field player. So that could still happen. Yeah, we'll have to wait and see, but obviously if Everton do a point of managerial this week then hopefully they will confirm this one and we can get on with our summer recruitment as well as moving players out, which is what we need to do as well. That is it for the news day and give the video a thumbs up, subscribe if you haven't and if you want more videos join us over on Patreon. See you later.