Hi everyone, welcome to the SmartQuest Seminar. Today our speaker is Dr. Panos Mottis from CMU. He's going to talk about bridge security. Let me remind everyone that our next seminar is next week. We have a speaker from CMU. We'll talk about the control of distributor and curate sources. Dr. Panos Mottis has been a special faculty with the Scott Institute for Energy Innovation at CMU. He did his post-doc there. He's been a scholar from CMU. His recent grants include one from the National System Operator of the Marshall and for the development of transmission expansion time and back. Another from Google X, an integral tool for electrical engineering. Between 2018 and 2020, he served as a recovery research professor that exists in Switzerland. This is a very great seminar that actually can stay as a major. Since 2007, he has contributed to more than a dozen other big projects funded by the European Commission. He received his diploma in 16 degrees in electrical engineering at the National Technical University of Athens. He has published more than 30 papers and contributed to five of the projects. He's a senior member of multiple IEEE societies, including the chair of the IEEE communications committee and editor-in-chief of the IEEE University. Good afternoon, everyone, both in the room and remotely. I'm very glad to be here. I want to thank CMU and Liano for their kind invitation and I hope the presentation today will be interesting to all of you. We're going to have an interesting discussion following. I will skip the formalities of the introduction. We did a very good job with reducing the progress that I had from Athens of the way to in the US for the past few years. I would like to give a different introduction of myself, what keeps me up at night and that would be the wider penetration of electrical grids and especially of those that are specific of a particularly volatile nature. To do that, we certainly need a wide range of two-degree method, optimization, control, and beyond research, we also need to think about standardization and other kinds of policy initiatives. If I would like to start with a take-home message for you, if you get the opportunity to participate in voluntary activities of the IEEE or by the IEEE of NASP and so on, please, jump on them and join. Your contributions are definitely going to be valued and propagating the long term across the industry. The preliminary question that I always open up this discussion is whether we do need artificial data from our machine learning power systems. The thing is that as we have been progressing throughout the various challenges and the new paradigms of energy operation, we're getting to have a more complicated electrical grid, a more complicated power system operation and market. At the same time, we come to the realization that no matter how many of the efforts have been and how many different tools have already been used, we still find ourselves in catastrophic situations like the 2021 Texas crisis that led to practically non-rolly blackouts, the California wildfires which were either caused by electric power system equipment or in order to avoid the cause of fires from electric power system equipment, the electrical grid would be preemptively disconnected and not serving tens of thousands of customers. At the same time, we are coming to the realization that metering infrastructure in general data and power systems is reaching up huge quantities with the 2012 data for the US amounted to more than 100 terabytes of information available. Now, the electricity industry is also considering very seriously how to incorporate artificial intelligence and machine learning in power system operation and not just in predictive analytics and non-operational level. This is the every initiative that started not more than a couple or three months ago and tries to see what is the path forward and what is the landscape that we should be considering as we try to more practically and kind of more aggressively digitalize the electrical grid. I'm going to start my presentation with one of my earlier works that is now doing the rounds again mostly because we're talking about how reliable energy resources are or can be. As a very short intro here, I will just remind to everyone what energy and active power is. Energy is whatever moves heat charges, elevates any device around us. But active power, which is the difficult part of that is the derivative of energy over time. So what happens at any given second? At the same time, we need to be thinking of the electrical grid as a voltage levels and congestion limits. Voltage levels means that all our devices, if you check the plug, they say they need to be operating some specific voltage level. This cannot be guaranteed just by itself. Naturally, we need to have proper control actions and operation while congestion falls on the side of the system where we need to consider how the existing resource that we have. So clients, cables, transformers are getting overloaded or they are at a good level of operation. This is important, these brief definitions are important and especially the, I would say, the dilemma between energy and power. Having the available energy to cover the demand is not enough. We need to have the available power. At any given second, the generation must equal demand plus loss. Otherwise, we're seeing increased wear and tear, damages and system collapse. What happened in the 2021 Texas freezes, what you see on the lower right part of this slide for a brief few minutes, maybe another one or two minutes. The whole Texas power have gone into a complete collapse because the generation equals demand plus loss equilibrium was not met consistently for quite some time. This is where the role of the capacity comes in. The capacity is essentially a market structure. It is the long term promise of energy availability at some point in time. Generating companies, investors are going to be handed out money to build new power plants at the promise that they're going to be delivering the energy of that plant at the time after that plant will have been built. So there is the underlying in window of a guaranteed active power out. And this is where it gets kind of tricky for volatile resources as wind generators, photovoltaic and the likes, because these kind of resources characterized by volatility cannot be by default considered into this framework of capacity markets and hence firm capacity. And this is by definition because practically speaking and with research advances that have been going on for the past 20 years or so we know that we can handle the volatility of renewables in very tangible and very practical ways. First of all, we can make the resources fully bespatchable, fully controllable and also we can integrate them in hybrid paradigms or even bigger than hybrid paradigms, virtual power plants, micro-regions, so on, and thus smoothen out if I may, their volatility. We know that and you have seen that. And the question is okay, we can technically in the broader sense do that practically. How does this pan out? Like what is the way to take the volatility of such resources and turn it around on its head and make it a firm in its capacity ratios. We need to think of the hybrid renewable energy system so we combine various resources with or without volatility, it is up to us and it is up to the probability analysis we can build on them. And in this hybrid, we start with a very straightforward and simplistic way. We can make it way more complicated, but let's start with a simple way. The n minus one, we have a hybrid of n resources. We know what is the maximum injection of that hybrid then given. Moment, let's assume that this maximum injection is lost. How do we handle this loss with the other resources? We need to read this patch, the other resources to cover for that loss. Simple as it may sound, it has a ton of complications because we need to make this repurposing of our resources economically feasible. So if we're selling our energy for six cents and the dispatching will make it 12 cents, well, okay, yeah, technically we are repurposing nicely, but if we do that double the cost, no one is going to be happy. Essentially the guy who is owning that hybrid energy source and wants to make a profit. And on the electrical power engineering side of the problem, we need to be respecting voltage limits, congestion limits and be prompt in such a dispatching. If we lose the resource, we can wait for hours until we will dispatch the other resources to cover for that loss. We need to be prepared. All of this creates a quite a quite complicated landscape of how to address the problem. Let's break it down in aspects for the for the aspect of the electrical engineering challenges, which is of course what is the status of the grid in terms of loading voltage limits and so on. We need to rely on some kind of forecasting which will come with uncertainty. For that reason, we need to account for multiple scenarios and treat volatile resources in a non conservative way. Otherwise, we're going back to square one caretale, whatever is volatile to avoid any challenge. This doesn't make any sense in this new part that we'll try to discuss. As for the optimality of cost, which is an empty hard problem for the electrical grid due to its non convexity, we need to think again in a kind of smarter way and tackle this matter maybe from another perspective. And so, as we are thinking of these challenges, what can be done in order in order, first of all, to handle the grid uncertainty is to be robust to that uncertainty. When we are accounting for multiple scenarios, we need to explore the feasible space of those all multiple scenarios and plan the best possible of them for as many as possible. So we are not just looking for one dispatching. So we lose one source in the hybrid and we commit the others to cover the loss. Just one solution to that might not suffice. Why? Because if we have other volatile resources that may also fail, we need to have backup plans for them. As you hear it right now, you're thinking, okay, so we're doing essentially a domino effect backwards in the hybrid. You'll see that in the way that this is eventually solved, it is kind of not as reliant on volatility and solving itself through the structure of the solution. As for the optimality challenge, the best trick we can think here is to avoid solving the actual optimization and resort to fast arithmetic solutions, power flows and so on, and work with that. So instead of solving for multiple different losses, multiple different optimizations, we solve, I would say, a relevant problem and near the vicinity of the previous status problem to make it fast to solve and building on that we also want to make it optimal under various situations. And here is where the binary decision trees come in. The binary decision trees come in the sense that they can take a set of data that are marked as true, false, zero, one binaries there. And by decision we mean that we take this data that are marked in a binary manner and try to understand the other attributes and how the other attributes affect the binary categorization. So this case is the example of food poisoning in a bucket. We have people who had like some starters, some desert, some main dish, and through this decision tree we're trying to understand, okay, what was the combination of diseases that led to poison like symptoms. So if you want to read this decision tree, for example, let's go on this part, it says here that those people who didn't have dessert one and didn't have dessert two but may have had a starter one were definitely not sick, only 4.1% showed symptoms. But if you read this part of the decision tree, all of these are really called rules. When we read a decision tree from the leaf all the way to the root, this is called a rule of the decision tree. So this rule, starting from this leaf, it says that those of the attendees that had dessert one, had main two, had dessert two and had starter one, showed symptoms of food poisoning by 98.8%. So we have a data set with multiple attributes, which are count B, which are the script doesn't matter, but the categorization that we care about is a binary one. In this case, healthy or with symptoms, zero or one, true or false. No matter how complicated the other attributes are, the decision tree will try to extract a verdict, a rule describing this zero one binary categorization by all other attributes. The most typical way to develop a decision tree is the so-called entropy, the Shannon entropy, I'm not going to be going much into the math because I'm also giving a link to the papers if you want to read it in more detail. So the Shannon entropy means that I'm trying to make the initial set image subsets as non diverse as possible. So at every step of the way you will notice that the previous true false proportion increases on the one end decreases on the other. So this is what the entropy does here, the Shannon entropy as a metric of non diversification of the data to get to as clean lives as possible. This is what the decision tree is just to give a reminder to anyone who hasn't heard about it for a while or sees a decision tree for the first time. As with all of course machine learning and potential tensions to have that risk of overfitting and that's why we might be able to advise to use either post pruning or pre pruning with various methodologies to keep the model for overfitting. Now let me show you the idea of how we solved the challenge of the firm capacity by hybrid renewable energy source in this field. What you see here that looks like an octopus on the right is actually the projection of the ACOPF, the problem that is underlying the dispatching of resources in a power system, project them to axis for your ease. So you need to think about this octopus in the n dimensional space as a non convex non linear feasible as you see there. The global optimum for our case the most economical dispatch over hybrid resource for a given load that we are serving is this blue dot. Okay, so this is where we are before we lose any power so let's assume that in our hybrid power plant we have a wind turbine and this wind turbine is like one megawatt and the rest of the power and the rest of the hybrid renewable source has I don't know maybe the other three or four megawatt but it is smaller portions. If we lose our wind turbine, the old operating point will be here and the new operating points are going to be in this new feasible space.  So the feasible space expands for the problem and we are trying to find the new global optimal, which is the yellow point here. So what do we do in order to approximate the new global optimum, we use decision trees in this way. We sample the feasible space of the problem with a power flow solution, not an optimization, just try random dispatches that are in the vicinity of the previous dispatches. So in this part of the feasible space, I have sampled various possible dispatches that work for my load, check, that work for my voltage and the other constraints, check, I call them true, and then I'm going to make some of these dispatches to be false. How I'm going to name some of them false based on their economics.  Then I take a decision tree, I train, I learn from the decision tree. How are these active power shape points described? You can see here for example that for the active power of generator I practically I am below, I am above this level. So I have to be at least on this part of the active power for generator I and at least in this part for the dispatching of generator J. Then I do the exact same thing again in the new feasible space. Again, find new samples, call some of the previous false because they're not economical and all the way I am going to be converging the global lockdown. For those of you who have some background in complexity and algorithms, you might have already guessed that what I'm doing here is essentially a binary search. I am doing a half space search through a physical region, and the trick that I'm using there is that because the space of the problem is non-linear non-convex, I use a decision tree to slowly narrow down to the global lockdown. That's how I have solved the problem in this case. I here have an example from one of my papers what I assume in one of the islands in Greece that I have installed almost a 10 unit virtual power plant. What happens if I lose this windpark on bus 43? Well, this is what the decision tree tells me. It practically tells me don't turn on the diesel units as you will see the cost of the diesel units is more expensive. And if I don't turn on the diesel units, I can cover the 70 kilowatts by the rest, which are cheap enough for the purpose. So this is how it practically works. I have essentially explained this through the example, so I don't want to be boring with these details. What I want to focus your attention on is the third bullet, which says that there are some recent proofs implying optimal guarantees for the method. I think it is a classmate of yours here at Stanford. I forget his name. I don't know if he's like a PhD student here who has offered the proof that if there exists an optimal classifier for an information problem, then there exists a binary decision tree which is realistically induced. This is big from the aspect that if you know that there is a perfect classifier, then you have the proof that there also exists a binary induced decision tree that solves the same problem. So you can actually solve it in a practical way. Of course at the cost of some error and so on, but this is the idea. So what we have to show here and this is one of hopefully figures cross may upcoming papers is that there can be an optimal classifier for a nonlinear noncontext problem and based on that we can have a binary decision tree solving the exact same problem. And I have a few papers on this subject you can read through them at your page and please feel free to reach out to me for any clarifications or follow us. The second part of this presentation is going to be focusing on artificial intelligence application so machine learning is one thing machine learning has to do with identifying estimating a function again. Artificial intelligence, on the other hand, is usually tools that try to optimize a problem, particularly Schwarm genetic algorithms and so on. What I want to say out of that is that as you must be guessing in most cases, either machine learning or artificial intelligence coming to flavors function estimation or optimization. However, not all practical problems are either or we have problems that have nothing to do with optimization, nothing to do with function estimation. And this is why we need to reconsider AI tools that are specific to industries or non typical problems. Otherwise, we are always forced to fit any industry problem in either as an optimization problem or a function estimation problem. Clearly, this will not work in all cases. And here is where it comes to the discussion of voltage. Now, the reason why we care about voltage in power systems is because of Ohm's law. When we have to transmit electricity over some lines with impedance, this impedance will cost a voltage drop. So the simplest approach to say, OK, how can I overcome the challenge of voltage drops? Well, what you can do is limit the flow of energy over lines, over impedances and bring that energy generation as close to your load as possible. And the biggest exactly on this gist that I'm offering here is where I come and conceptualize a new view of the distribution systems. Distribution systems are usually radial, medium and low voltage are radial. So what I come and say is, OK, I'm trying to forget that the radius has like these branches. I can lump them on this part and I can reimagine this distribution feeder as a road, as a physical object. And I can also model the loads as point masses, as point masses on this road. I will also then use the impedance of the feeder up to every point in this feeder and call this mine axis system. So instead of using distances in miles, I'm using distance in Ohm's. So I can rethink the feeder as a road with an axis system with their own orientation system on impedance and the loads and the generation as point masses. With that, I can conceptualize the center of mass of loads. The center of mass will be given in a distance in this case from the substation of impedance. So instead of having, as in plain physics, the distance of, let's say, something meters from the one end of the other end of the physical object, I describe this impedance. Now, why is this important? This is important because I can think of the loads as one kind of weight and the generation as a complementary kind of weight or mass. So I can separate these two and say, okay, if the centers of mass of the model loads are counterbalanced by the center of mass of the model generation, I am essentially reducing the length in this case impedance that the power flows have to go through. What did I tell you three slides ago? The closer I get the generation to my load, the fewer the voltage draws. So this model is just exactly that. You model a whole feeder of loads as a center of mass, a whole feeder of generation as the opposing complementary center of mass, and you try to counterbalance them. How do you do that? You just move around the generation. So the generation that was near bus 150 can be reduced and we can move it and increase the generation or start up a generation, a generator or a battery, discharge a battery, near bus 9. You see how the center of mass of generation with this adaptation of the generation near 150 and the generation or a battery or whatever we have near bus 90, it has moved the center of mass of generation closer to the center of mass of the loads. When you reduce the length of the lines that the power flows have to go through and thus reduce the voltage drops. The idea is broadly as I described to you, but those of you who might be a little bit more observant might tell me okay, do you stop when you move close enough the center of mass of the whole feeder between generation and load. The answer is the latter. You will first counterbalance the centers of mass between generation and loads for the whole feeder and whichever part has been counterbalanced, you move on to the next part and you will do that a couple of rounds until it gets all the voltages within limits. So what you see here is that the initial state of the voltage levels is the bottom line in this graph, which falls below the acceptable level of 0.9 per unit. And in this feeder, I have re-dispatched active power of generations and maybe used some demand response or whatever else I had. So as the centers of mass of generation and demand are counterbalanced across the feeder and in lesser parts until you can see that the voltage levels have been moved completely above the 0.9 per unit. So you see how in every step of the re-dispatching, I was improving the voltage by a tiny bit all the way to above the 0.9 per unit as would like. This is the publication. I haven't worked more on it because I haven't thought of any other way to improve it. As I said in the starting slide of this second part, it is neither an optimization problem nor a function estimation problem. So the improvement I can do has only to do with how the conceptualization might be improved. And now let me gather for you some points and we can ponder on them together. The reasons for artificial intelligence and machine learning uses in power systems are by now crystal clear. The energy portfolios are very diversified. They have a lot of stochasticity in them. We used to have only stochasticity from loads. Now we consider stochasticity from renewables, from diverse stakeholders. There's not one utility. There are multiple now entities in the electricity market and also have large amounts of data that are just sitting there in most of the cases. For artificial intelligence and machine learning tools per se, there are the opportunities for the optimal control. As you are already aware of the ACOPF is a nonlinear nonconvex problem and we have to solve, not merely solve even with the most advanced relaxations and other mathematical structures. The stability problem is a problem of local extent that can expand all the way to a fully blown grid collapse and power quality is becoming an aspect as well as we are moving to more and more power electronics devices. Before the path forward, there are challenges and considerations. One of the challenges is a very hot discussion at the moment on whether we need the AI and ML tools to be interpretable. Interpretability means that we can take a tool and as we're discussing, oh, this is mathematically provable by that. One, one and one equals two, and this is not a black box. We would like, we would desire AI and ML to also have a kind of open knowledge structure, to not be black box. But any of you who has worked with AI or ML in the single picture understand that after some size, it almost becomes like a black box due to complexity, not because we actually want to be a black box. The lack of standardization software engineering lacks the new ones or the standard or the patent. You can copyright it, you can describe it in broader mathematical frameworks, but it is more deep, it is kind of more difficult to standardize it. The standardization will also be a very interesting challenge to address. I'm saying that because my good friend of mine who has not been to use Spirushka's Vasiliyadis is trying to offer tangible data and proofs to show, for example, how neural networks can be translated into linear functions. So if you can give this analogy between a tool that is in the broader context, black box and say, okay, stop black box, it is just linear functions and this is how you interpret that, you offer a helping hand to utilities and operators to better use them. We also need CRISPR guarantees of robustness and optimality. This is something that because we still consider electricity, as is water and other things, a social welfare basic product, we cannot just say, okay, yeah, let the market mechanism work this out, we need to have the lights on, we need to have water in our taps. So robustness and optimality is a necessity there. We need to also open up the academic research more to practice in the industry. There is some silo siloing there that has been creating, I would say at the very least frictions at the very at the worst difficulties in understanding each other's starting points. And of course, support initiatives as that for every end with as I said in there, my introductory slide, a joint voluntary activities with the IET and the various other engineering and computing bodies around the world and around science. And we thought I want to thank you for your attention. I want to thank you again for having me here. And I hope I planted some seed of thoughts to all of you and we can have an interesting discussion going forward. Thank you very much. I was trying to understand the second half of the presentation about the mapping the geographic physical grid that like the parents of a feeder to the resistance based one one dimensional, and I was They go there. You said that it wasn't an optimization problem but then I was wondering what was this shifting things around to the voltage got above a certain level. How is that similar to or different from optimization that sort of that changing I mean I get this this concept of the mapping that's really cool and valuable by itself. So, this is not optimization in the sense that the underlying problem the voltage problem is a problem of locality so you can solve that you can solve the problem by locally counterbalancing by locally compensating the losses caused by a flow of power flow of current. But in the broader sense it cannot be expressed as an optimization problem, because we go back to long convexity and so on. So, in that sense, the voltage problem is conceptualized as a problem of local resources matching, which to model local resources as if you're solving multiple different optimization problems. You want every locality to be solved. One optimization problem here, one optimization problem there. However, if you think about it, the locality is as we have like an impedance connecting all of them is leading them as well. So, you would be, if you would like to translate this optimization problem, it would be at best case scenario, the part of optimization. So you want or even worse again. So you need to make sure that everyone is happy and no one is falling behind. So this is why I was saying that in such kind of problems where the optimization model might exacerbate the size of the problem, you need to think in its root. What is voltage drop problem? A local grid point problem. And that's why we hear about, for example, capacity compensation of the stack of the feed and so on. So this local generators from the other end of the country can send reactive power, but in the meantime, you have spent all over the overhead lines. So this is, let's say, the idea there. So I'm not, I'm not rejecting the, let's say, the view of the problem was an optimization, but you would make an optimization problem by making the same time very complicated to actually solve it in a meaningful way. So this is like these steps that you hear that you see here, for example, take less than a second to solve. And if you think that the fact that the, first of all, the resources in the distribution system are relatively fast, photovoltaics, batteries, demand is sponsored, just turn off the suits and that's it. One second of solving the problem, combined with the fast dynamics of resources will amount to no more than a few, not even more than a dozen seconds to bring such a system within the proper quality level. So that's why we try to also avoid lengthy procedures of solutions in power systems because it reflects to the operation that needs to be kind of following along. So just to make sure that understanding you're saying that this conceptual framework allows us to look at the problem as many sort of separate local problems as opposed to this one big mobile optimization and that's the role of the conceptual mapping. Exactly. It's like a very simple optimization in a small neighborhood. Exactly. Got it. There are some questions online. Sure, I can read them, don't worry. I'll show it to everybody. Okay. How do you want to describe an artificial neural network as a linear function? It is completely false because the beauty of the NN season is not only that by activation functions, it's filtered like convolution, please clarify that. Again, this is not one of my works. I refer you to the work of Spiros Vasiliades at DBU. What he has been trying to show is exactly that, like how some neural network structures can be modelled as linear functions and with that feed it as a premise for the power system operators to have, let's say, I would call it friendlier feelings towards machine learning tools. So I do understand your objection there, but I could not expand more and this is not my work. The load mass techniques is given in theory does not have the capability to implement it in real operation, the calculation of its integral is not easy. If I understand correctly, the objection there is that the approximation of the center of mass modelling is difficult to calculate. I agree on the aspect of the integral. If we're going to be solving this as a problem of physics with distances and weights, yes, but if we model the electrical grid as a distance of impedance and we tab the loads and the generators for their total impedance from the infeeding part of the substation, it is easy because it's essentially, if you want to model it and I can even pull up my paper for this, you calculate impedance multiplied by load and then you divide all of that by the load, the total load of the feed and with that you can approximate the center of mass as impedance. So I do agree that if you take it in the physical sense, it is not easy, but if you use the physical intuition to project it as a model of a new system of axes, it is not actually that difficult at all. If there is a follow-up, please feel free to unmute yourself if you are in the chat or something and feel free to ask me, I would love to follow up on this. You can see in the paper that I pulled up, you can see all the details of the calculation of the center of mass modelling. Another question showed up. The question is, do you assume a radial network for computing the center of mass? As I said during the presentation, the example that I had in the paper and I also calculated was with a radial system, so I would take a distribution feeder, I would lamp up the branches, so I would start first with a depth first sense. I would start with a depth first sense to find the longest impedance in that feeder, and that would be my modelling of the feeder as a rod. All the other branches would be lamped up with their loads and everything on the main model of the feeder as a rod, as a physical object, and I would work on that. From calculations and preliminary tests that I've done at the time, I've been able to show that we can go also to the two-dimensional. So, instead of considering the distribution feeder as a radius, it might be also a loop, a mass network. We have to go there though at that point on the two-dimensional model of the feeder or of the grid as a physical object to attach to it points of mass models and do this counterbalancing between generation and demand. Any more questions? Thank you very much. Thank you very much.


Mae'r bif suicidal sydd y maen o'n gyfr Mae gennym i'n meddwl yn ystyrgynt. Rwy'n canfodol i'n meddwl, a ffanaf i'n meddwl yn ymddangos. Rwy'n meddwl i'n meddwl, wedi gweithio i'r meddwl i'r meddwl am ffanaidd i'r meddwl a'r meddwl i'n meddwl i'r ffanaidd i'r meddwl, bod yn cael ei d sor usandoid y ffordd. Yn gwneud ei gweld i'r gwneud ddweud wedi gweld i'r gwneud ei ddweud i'r meddwl. Roeddwn yn credu cael ei ddweud ei ddweud y ffordd, The rich countries are coming into the steel. So all this invention, the profit from invention, is being made by the rich countries at the expense of the poor. You get this permanent argument that doesn't really have an ending between the farmer fear and developing country together feeling the principle of IP protection is working against them before the developed countries. There isn't really the red solution for that, ac mae'r cyhoeddedd Cd-D yn ymdill ymddill. Mae'r gyflaen oedd gyrraedd yma yn ystod yn gweithio i gweithio eu gwerthodd. Mae'r cyflaen oedd yn 1993 ac yn y gyflaen o'r cyflaen o'r cyflaen o'r gyferllin. Mae'r bwrdd y cwestiynau chyfodol yn y cyd-wyr, yn y pender, yn cyfridd yma, ac mae'r cyfridd ar gyferllun gyfrifau, yn gyflaen o'r cyfridd, yn y cyfridd yn y cyfridd. Mae'r cyfridd yn y cyfridd yn y cyfridd, yn y cyfridd, yn gallu ddweud y brif yn gweithio ynglyn â rhwylo sydd yn cael ei ddweud, ac mae'n ddegos i'r pethau yw'r unol yn dweud hynny fel y dyfodol. Rwy'n ddweud, mae'n ddegos i'r ddweud o'r ddegos i'r ddegos i'r ddegos i'r ddegos i'r brif yn cael ei ddweud. Mae'n ddegos i ddegos i'r ddegos i ddegos i ddegos i ddegos. So, if anybody is worried about the change in the IP philosophy really been very Очень having producing public goods to protecting the IP just think that this new approach to IP protection was exactly what the anti-irrig NGOs want us to be they want us to take control over our IP so that we can prevent mis-use by the deep真的是 And that means well enough Felly arno, mae'n ddodd yn gyhoeddur i'r ysgol yn gwneud o osуys i ddweud, ..wyddech chi'n chlasio'r ffyrdd... .. perfectionist yn fyddfa yn siaradeth. mae'r cyffredin er mwyn i'n ddweud o'r wneud. Byddwch chi'n nhw'n bryd yn fyrdd o'r prysgol. Yn ni'n fryd i'r lleol ar yden, cofio'r sgol yma? Mae'r ymdelineis yma arall eich bod yn ynmysgol yma. Dyna llawer o'r ffordd ag unig o'r gweithio gyda'r golygu, eupon i'r gweithio gyda'r golygu. Ond, mae eisoedd o'r gwir iawn yn ddal, ei fod wedi'u gwasanaeth yn gwneud i'r ffordd. Yr unig o'r gwir iawn yn gwneud ei gweithio siarad. Ytog amser. Ac wedyn ond nad ydym ni'n gweld ar gyfer eu ffordd. Ydyn ni'n fwy o'r ffordd a roedd ni'n ei wneud i, ac ydych chi'n ei ffordd i'r Fygoedd. if you're sending it out, it turns out to allow others to do their own brewyd. That's a little bit more complicated. Depending on whether it's a genbind next session or anything computer-less you receive for the SNTA on the other non-irey line, this is a difficult one. Different kinds of breeding lines, or at least the area of variety. They require different kinds of energy used. The difficult one is this. If you've got non-irey material and you didn't get it with SNTA, Fe wnaeth y gallwch yn uwch i atweithio'r lliwyr arniad wedi eu siam arall, a'r ysgolweld yn gweithio'r cookedwyr, mae'rael i gyfer ychydig yn ddeill sydd honno i chryfodau'r hyffordd fawr o fewn o'u gwirio. Dweud oherwydd y pwynt wedi'n gwirio'r hollol yn ei gofal o Pythoedd. Mae'n ffarnwys yr oed yn amli gymrydol i weithio mor ffarnwys i'w gwirio'r hyffordd. Dwi'n gofyn yn mynd i gydig yn gwirio arno. If you want to take things from farmers, it gets a little bit more difficult. You want to take a germplasm from farmers, you've got to comply with the national legislation on bioprespecting, and that's different in every country. What the CBD requires is government level what they require, what they call private informed and central pick. You've got to get the government to give their own consent before you do this, and it's got to be based on information. You've told them what you want to do it for. In the way of critical, which is the recent extension to this, he encourages you also to get picked from the farmer, and a lot of countries require this as part of the national legislation. The treaty tries to make things easy, it invites all farmers to share this with you, and treaty member countries are obliged to take steps to encourage the farmers to share this with you. These haven't really been awfully effective at the moment, they're still pretty difficult. If you want to get germplasm from the farmer, you'll need to make sure with the national authorities of the country that they're happy with you doing this. Taking traditional knowledge from farmers, the thing that SSD people do, is a lot more difficult. On certain, it's been the subject of pretty intensive discussion, intergovernmental discussion for 20 years or more. I've put it previous here because they don't seem to be much progress. They all say this is really important to do. We have to protect farmers and traditional knowledge, the authorities to know what that means. So move on now, how to conserve that. This is another area where people tell us how to do things, because they think it's really important. We can't be left to our own devices to do it properly. The standards were first defined officially by FLAO as a PGR as it was in those weeks back in 1994. Bystanders have just been endorsed by the FLAO in 2013, but these are all based on committee decisions. We haven't had much scientific input into them, so what we find is that they're not actually adequate. So one of the exciting things we've done over the past few years is to set up a new conservation research unit to address this problem that actually we need to improve the standards. Here's an example of germination rates after a number of years, a varying number of years of storage in the human rights. Of course, most of the germination rates are up here in the 80-100% range where they should be, but actually far too many much lower germination rates in the human rights. We need to sort out why we're getting so much inadequate germination. One of the things we're finding is that it varies with genotype and environment and first harvest processing. Here's an example where rice has been held in temporary storage for either a short time or a long time before we can process it. We have to put it into the cold storage and you see that if it's undergone a long delay, then the germination rates of survival under storage is much worse than if we could process it quickly. Sometimes we're fortunate because a lot of you want germ pleasant for us. We try to make that the priority. If people want seeds urgently, then seeds, like seed-persons, they get put to one side for a while while we're handling your requests. That unfortunately has this knock-on effect that the remaining seeds don't survive with it. There's genetically environmental effects. Here are four varieties being put through accelerated aging tests where we put them in strongly aging conditions for a short period of time. The black lines show seed-producing the tri-season, the red lines see-producing the red-season, the red-season, the red-season, the red-season. For four different varieties we've seen for two debonico times. They're showing a very short lifetime in terms of this house time. So there's genetic variation, there's environmental variation, and somehow we need to sort out this quite complicated trait in order to maximise the longevity of the seeds. We need to scale up capacity because we're expecting much higher use of accessions, genetic stock, particularly in the future. So we're looking at things like automated germination testing, which we are just looking at right now. Automated characterisation that is getting grained and whipped through image analysis, which is almost there. Just like that highlight. Notice you can see the individual hairs there, getting this kind of high-resolution picture becoming critical because when you can make the computer distinguish the actual edge of the grain rather than seeing this as just a fuzzy boundary. So there's a whole load of areas that we're hoping to improve efficiency, which allows us to keep the machine in supply more effectively. Which leads on to the last bit of what I want to talk about, the usage. Everyone says we need to use the collection better. To me, about 85% of our accessions have been screened by area scientists for a long time, or sometimes over 120 times. Last week Bob said it was underused, only 5% were used in reading, and that's true if you look at crosses. I don't know whether you consider this a good nutrition rate from 85% being evaluated to selecting 5% that are considered good enough for crosses. It might be appropriate, or you might show that what this is showing is that we're not able to choose the varieties better, but we'd like to be able to give accessions to breeders to all likelihood that they're used. But I would just like to highlight these gaps. If you're interested in testing some accessions that we've never tested before, we've got 16,000 old accessions that's never been evaluated since 1985. Things have changed since then. Maybe if they were evaluated again now, we'd be able to find them more interesting. There are 12,000 collections, accessions that are collected after CBD that have not been evaluated, which is the majority of them. This is interesting because after the CBD there was concern amongst the breeders. A lot of the breeders decided they didn't want to lose gene bank accessions because of the CBD, and that's reflected in this very high percentage of these closely-reduced accessions that have not been evaluated. Now that we've got the treaty in place, we might like to consider having a look at these things that have now been looked at outside QSC. We've got a small percentage of those treaty accessions that have not been evaluated, that should not have been evaluated, which then have been evaluated. But they're starting to come into the new varieties that we have in the book. Here's just the origin of the old varieties that haven't been evaluated since 1985, but in 90 different countries. Quite a lot from China, no one's looked at the 30 years. Just have a think if you'd like to try. These closely-reduced accessions, a lot from Laos, not so many other countries, and about 20 different countries, but still a range of universities. One thing that's been around in gene bank philosophy for a long time is that we need to characterize them, because that's what makes it useful. You know what they're like. We will use them all. And we have gone to a lot of everything in gene bank to characterize them. These are figures that are a few years out of date, but we've already done the 95, over 90% characterize book. Most of the standard traits, but these tend to be higher heritage traits. We look down here that they're in the same order. But these are not particularly of having a lot of interest to most breeders, until you come down here to these to 80% of the flower. That's an important point, which is up here, is having a lot of data for that. But a lot of this has turned out to be not of much interest to most users of gene bank. So it hasn't been nearly as effective in promoting useful gene bank. They used to say, the second thing we've been told time and time again is that we should publish our data online. Say, here you are, you've got a database for you. You can choose it. You can choose what you want. And users ask us to do that. Other gene bank organisers ask us to do that. But actually, when you look at the kinds of questions we want, you look. These are typical of a real request we've had. But as I said in the countdown I told you, there's so many different variants of these that we've just asked us, but because of that I won't know what to give you. You ask, as I said, I won't know what to give you unless you tell us which particular one. Nice that's good for growing in Canada, or UKs, or Ethiopian highlands. Any of these things, droughts, chief blood, climate change, I can't begin to tell you which is the best accession solution. How would you convert these kinds of requests, think that we just want, into a database search? That's not a trivial exercise. I'm sure it would be worth doing, but we haven't yet found any online search mechanism for any gene bank in the world that actually translates what we know about accessions into what we just do such as we want. Another thing we've been pushing into doing is making call collections. There was a paper a long time ago that claimed that we could get money for something worse with 10% of the collection, which is pretty much a figure for a matter of the year. They were saying that would give you a small amount of collection to evaluate, but still people find it 10% to be. So if our collection is about 12,000, you're not going to evaluate which amount of accession is. So that people have invented mini-cores, micro-cores, and nano-cores. We have call collections over the whole range of 20 up to 12,000 accessions. You're welcome to use any one of them if you like, but why I've put down here, this is a notion of theoretical frequency distribution for value for a defined objective when you're not in particular trade at a location and you're located at a production system. You're wanting to get maximum value. This is a frequency distribution of gene bank accessions. This, almost by definition, is what a reader's collection could look like, because the readers have already gone through picking out the best. If we try to argue that their collection wasn't better on average an hour than the readers would have been able to do. Moreover, they have tested it for their purpose and we haven't tested ours for our purpose. So why on earth should you waste resources on evaluating material that you know has a low average value? And the same question applies to a call collection, to a small subset of collection. What you really want is to get up at these valuable accessions that are better than anything a reader has. But how do you find that? You either need some very exploratory reader, or you need a gene bank into a different way of doing things, a lot of some of the readers. Some old readers who are close to retirement will be much more cautious and say, I'm not going to follow a reader all this useless material. Another strategy is to use GIS methods. If you know where a traditional address came from, you might go to believe something about its properties, something from high up in the pool is getting more co-towered than something on the pangas. So you can, in theory, use GIS approaches to better selection of accessions. But actually, when you look at what we've got, this is an advantage to accessions where we know data on these gets a passport data. So we know the country of origin from what's almost everything. But still, even if you go down to another province, there's only half the collection of your own province again. And when you look for that longitude, there's only about 40% of the collection of that longitude collection of data. So applying GIS technologies is only going to work for various kinds of subsets. So it is not particularly effective in practice for most of the collection. Another one that GMATs have been told to do for many years is to get into pre-breeding, where we cross some clear accessions into elite genoclasm to make them more attractive to readers. I'm thankful that we don't have to do that, because BBGB has been doing that very effectively for many years, bringing in new genoclasms, especially for the purpose of making it easier to other readers. The next one is phenotype. Everyone is saying we need to phenotype. That's the big bottleneck, is understanding of phenotype. But actually, we can't do that in the whole collection. It's not even one train. If you look at the difficulty of looking at the stress tolerance, you might have low heritability, you might need lots of replication, you might need lots of locations, you might need specialist equipment, you might need unstressed and stressed treatments. It's a huge task to leave it for one train, and no one could ever contemplate screening of 120,000 concessions. There's specialists in those stress measurements. You can't do it when you're reading the human access. And it's very inefficient. If you're looking for code points, you're not going to start with concessions on sea level in the tropics. And more importantly, again, the value of concessions actually lies in the phenotype of its programming part of itself. We can't think about what ways we can produce this better than our concessions. It may be okay if you're looking for major genes, but the phenotype of the concessions will really indicate what it's functioning like, but it's really not a good predictor when it's looking at small-effect ptls and genes that have a fantastic interaction. So it's not very effective if you're looking for transgressive segregation, and that's what we need to be looking for when we're wanting to invent new varieties that deal with new problems. We've got to have transgressive segregation. So we really have to phenotype the concessions. But if it's impossible just to phenotype the accessions, it's even more impossible to phenotype all possible, but it's really out of question. Which leads us nearly to the end to get very quickly about the way we're thinking about it. If I went into this in proper detail, this would take another seven hours on its own, and I hope Ken will do that one day for all too long. The DNA has a very active characteristic for the gene-like. That is, 100% heritone, more or less, we can, in theory, measure the known accession, and that's what we need to have an indicator of what an accession might be useful for. We must have data on it. If we don't have data on it, then we've got no idea what an accession would be useful for. And as you know, the one that happens with phenotype. So the idea now is that we develop the training set during the gene discovery by sequencing and phenotyping our core sets of between 23,000 accessions, and they're probably using all sorts of methods, the magic, the nest association of the population, the wide range of the population. The mutants hope to see for mutants that they're trying to discover the function of every gene in the rice genome that will contribute to this and that of the relations between genotype and phenotype. Once you have that for this core subset, you can start in saying, well, we can make predictions about the potential value of the other accession but we haven't. And Bob last week was talking about theory 2035. This is what we invented just theory 2035. We'll have all accessions for you. We won't have all accessions phenotype. We'll never have all accessions phenotype. But if we have this knowledge, we can then, we can combine our idiotite, whether it's a new plant type or whatever the idiotite is of 2035, we can use this knowledge to convert that phenotypic idiotite into a sequence idiotite and design that on the computer and then compare that ideal sequence with what we have in the gene bank and we can construct the best reading system, which accession will be most efficiently to that new sequence that you've designed and we can use it to not only to choose the best of the parents but also to design the best of the markers to use the marker-assisted solutions. And we will only ever phenotype to validate or refine the predictions. Of course this will be cyclic but first predictions aren't going to be very accurate but as we do not have the knowledge about gene type of type of relationship you can gradually get better and better and better. So you will always be phenotyping with only guided phenotype, not just phenotyping anything, phenotyping in order to help us in this time of relationship between genes. Getting to them is a big thing, it's almost like saying being the upscotty is that kind of thing. There's still way in the future, it's not going to happen to long after I've left you in, there's some very big data management and informatics decisions required, we're going to have to come up with normal analyses of the genes in this type of relationship. We have already started putting genetic stocks in the gene bank, and we think we're pretty keen to helping to build up this knowledge of gene type in the time. The reference genome is a big problem, the fact that we've only got little barrier at the moment, we need to be building up much better references that will be useful across the entire rise of gene. That's a very important short term target is to improve our reference. Of course sequencing technologies are going to improve and we need to be both erosion of the time, hopefully reducing the amount of assembly required to work from that point on. We need, in the medium term, to optimise our pipeline to discover it first. We're not going to just say let sequence our collection, we need to find out an efficient way to sequence how we do the first 6,000 and the first 10,000 and the first 20,000 and so on, and optimising the gene fleet type prediction system, just what kind of algorithms do you need to get the predictions? That allows us to do these things. I'd like to acknowledge the GRC team, everyone involved in this, but not only the GRC, everyone else who contributed to Theme 1, not only those who didn't. No one contributed to Theme 1, but a lot of people out there. We had to be part of Theme 1 without knowing it. Anyone who's evaluating and using rice diversity is helping us to get there, and even those who have a view of political and legal stuff, please help us through this. So, empty very much.


day one of classroom setup vlogs. Hopefully I will be done with this classroom by Friday. Today's Monday and I have daycare for the next five days. So I'm gonna take advantage and I'm gonna try really hard to get this room set up and finished before Friday. So I'm just gonna take you guys around and show you what I'm currently working with. It does not appear that the carpets have been cleaned. There was an email sent out about a week ago saying that they weren't done, they didn't know when they'd be done. But at this point, I'm like, I don't really care if they're done or not, like bring a vacuum in here. It's fine. My carpets aren't really that dirty. So I'm not gonna worry about it. I'm gonna just start setting up. So I'm just gonna show you guys what I'm working with currently and sort of like my game plan for the day and then we'll get started. So this is everything that I brought from home. The tubs are not staying here. These were just vessels for me to get things here. Most of the things that you're seeing were in my back to school hall. So if you are interested in watching that, I will link it in the iCard here at the top. And then my bookshelves, or not my bookshelves, my cubbies have been covered. I will uncover those. I have some furniture that I need to put together. So I will probably do that today just because I do have my drill and it's nice in here and it'll kind of postpone me moving furniture right away. This is all of the furniture that I have. It's kind of just pushed up against the wall. I think I'm probably gonna get rid of this table and just give it to Mandy next door because I have an idea for my small group area which will mean I don't need this table for myself anymore so I can get rid of that one and use this one for students. I'm planning on moving this piece and this piece over here where my backpack hooks are. I'm gonna take them down. That's one of my projects I wanna do today. And then just furniture. I wanna get rid of this. So I'm gonna roll out a table, empty this and put everything this in it on a table so that I can put it on the to-go list. But this is what I'm currently working with. Everything in the middle is gone. I'm planning to try to do a wood wall but I don't think I'm gonna do it. I think it's just too much work. I don't think that it's really worth it so I might just take this board down and recreate it since I'm gonna have my tall guy back there and if not, I might just leave it since it's like the perfect size, we shall see. I think the first thing that I wanna do is start building some furniture so I have this guy I need to build. This is a storage piece and then this guy that I need to build which is one of those Ikea eight square, eight cube bookshelf things. I'm gonna use that for my own personal desk. So I'm gonna get started putting furniture together and we'll enjoy today's vlog. All right, this piece is done. Now we're working on that piece. Mandy's now here. How are you? I'm sweating. So we're working on furniture. I have this piece and then a cart left but the cart's like super easy. And then I'm gonna start moving stuff. If I didn't vlog it, didn't. In the back of the room, I still have to pull those two but I'm gonna start piling stuff that's inside these and on top just on these tables because I'm going to move this and this over here. I'm gonna take this wall down and remove my backpack hooks and I think what I'm gonna do is put this wooden guy here and then this long one right next to it. Take this, whoa, are you okay? Are you broken? Okay. Take this wall down and then center it over that long thing. That way I have room for my backpack bins right there. Why are you laughing? Did you, what did you do? Just don't sit on them. Oh, it'll make the cut 100%. Okay, so the mess has to be bad before it can get better. So all this bookshelf and this bookshelf. So I'm gonna leave it all here until I'm 100% certain that I'm leaving the furniture where it's staying so that I can put all of it away. I also have to go buy two smaller bookshelves and I'm gonna get these same ones that Mandy has cause they were $15 at turn. I'm gonna get two of those to put my library on. Jade will be in the window again just like last year cause she loves it over there. And then this is back here now and there's space for my computer cart right there. So that is perfect. And my printer and this is getting removed right there, that thing. So I think we're looking good here. Has to get worse before it gets better, right? Hey guys, okay, so progress over here. I'm gonna do my library space here. You still have a long way to go before things are presentable. So right now I'm starting to unload these bins and sort of organize things onto tables so that I can organize them later. So I'm putting binders and like curriculum on this table. I'm putting containers and storage on this table. And then I'm gonna start putting some things inside other bins to now then put away. So yeah, I don't know if it's gonna stay this way but for right now this is my desk space. I sort of kind of wanna put the two black tables in front of it, but I don't know yet. The beauty of the tables is that I can move them so long as I keep them clear and have all the stuff that I need access to back here and just keep the table tops completely cleared. So I also don't know if that's gonna stay there. We will see. All right you guys, so it has been a super long day. It is now five o'clock. I've been here since nine and so far, so good. I think I'm gonna be ready to finish bulletin boards tomorrow and so I'm gonna turn the camera around and show you guys what I've done just to summarize the day. So I really wanted to do an accent wall but I knew that this wall was gonna be really difficult because it's all the way up and down and I also really wanted to use this shiplap paper behind my desk since my desk is all black furniture. I thought this would be really cute with all the black but I knew I couldn't put two different shiplap colors on one wall so I decided to do this entire wall wood paper and I think I'm gonna do this entire wall black paper with colored borders and like make a few different bulletin boards that go all the way up and down. Like I'll put one giant board right here. I'll do a board there.  So I did this all by myself. I did get a little bit of help here and there with a couple of things but for the most part I did this by myself. I like it. It's like the smallest wall in here that doesn't have all the way top to bottom because this whole spot right here I did not go all the way down. So like if you look behind it you can still see blue in there but that will be covered. There will be something there anyway so. Yeah so tomorrow I'm gonna focus on getting bulletin boards put up. I'm going to finish my library. I still need to get two more bookshelves because of all these books that I got. All these books. And then those two boxes in there are full of books so I definitely need more. My computer cart's gonna go right there so that space needs to be empty. And then I need to obviously put more stuff away. All of this on this table is decor that I need to like either pitch or put up. All of this is like curriculum binders that need to be emptied. And then over here is empty bins that I can store things in. Over there is more like curriculum books that I need to just put somewhere. I also decided to do these two rows or columns I guess of cubbies to be storage. So I have like my lit sets in there, my Arizona textbooks, my dictionaries and I'll put some more there and then do 24 cubbies going this way. Because this table's kind of in the way if there were a student like this student wanted to get in his cubby while this was going on he wouldn't be able to get in there. So I figured if I start the cubbies right here then this table isn't blocking. Also this I need to paint. Ooh, that's pretty good. I need to paint this black. I'm gonna put some indoor plants on that and then do some more storage in there. And yeah, this is what's going on in here today. I decided to put this in here because I am gonna start trying to get up early and shower and then just get ready at school. So I bought this on prime day with like 10 bucks and I'm gonna keep like another curling iron in here maybe like a brush, some makeup just so I can get ready and I put all my nurse things in here so like first aid and then these are like obviously things that kids might need. And then I plan to get an over the door mirror to go here just for myself so I can get ready at school because I live so far I like to get ahead of the traffic which is why I leave super early.  That way I can just have less traffic. So I'm gonna turn all the lights, pack up my stuff and get on out so I'll see you guys tomorrow for day two of question set up.


Time here for more systems. It is September 12th of 2023. And we're going to talk about invalid traffic. Normally, in the context of the content I produce, you would assume I'm talking about network traffic. But in a way, this is network traffic. It's just the label given to traffic that comes to YouTube that is allegedly invalid, usually bought by some type of link farm, people who buy views to rank a video higher. Now this is a constant cat and mouse game YouTube plays with creators who are trying to scam the system. And we'll just call them scammers, not really creators. That is not the type of content I produce or many of my friends. And this has been a problem that's been going on for a little while. That's why I mentioned a day to beginning. It's hit my channel, but it seems by going back a little bit, it's been hitting lots of small channels, but ramping up to bigger and bigger channels. Now I feel really bad because the small channels have had loss of revenue from this bug is much as 90% of the revenue. And my losses already are about 30% of my revenue has just disappeared. And there's no really way of getting that back or knowing which videos it is. But according to YouTube, and they have been as helpful as I think they can be with some limited knowledge. They give the answer that when you have the invalid traffic bug, there's going to be a video labeled invalid traffic because of, you know, the bugs they found. And you should stop doing that to the video. But I'm not finding it on a video. I'm just getting an error on my channel in general. And normally I can ignore the weird errors you get with YouTube. But this time I can't because it's taking away revenue. So I'm doing a video on it. Now there's going to be someone that already says YouTube's going downhill, et cetera, et cetera. Or I've seen people try to claim that YouTube just hates smaller creators. And they just don't care with people with less than a million subscribers, et cetera, et cetera. I don't think that is true at all. Matter of fact, I would say to most all those people never attribute malice to that, which can be adequately explained by legacy code. The YouTube algorithm is a rather complicated system. I've not seen it firsthand, but you can probably assume the absolute insane number of videos being uploaded. You can't audit those all by hand. So there's obviously a lot of automation that goes into this. And sometimes they're going to get it wrong. We're all human. We all make mistakes. I don't think these mistakes are specifically malicious. And YouTube is still the best platform for discoverability. And the only way I can really, in a scalable way, get my videos out there, get them discovered and get them viewed by audiences. Therefore, I try to work within the system that is YouTube until a better platform shows up. And I can join that and people can discover my content over there. But for today, this is the platform we're dealing with. Now I have contacted Team YouTube and the YouTube liaison. They both replied within the context. I think they gave me adequate answers for what they know, but I think there's more to the story. Now this happened a few months back to some small creators, but there is a tipping point where it started to accelerate a whole lot. And that is the Spiff and Brit livestream. Could be coincidence, but I think it might be a little bit more. The Spiff and Brit, who I had never heard of, did something rather interesting about a month ago and then did a follow up video giving more details. But the too long didn't watch was he started a live stream. He figured out that if you open multiple windows and multiple tabs and start hitting refresh on these tabs and you as a single user logged into a single account can count as well as many tabs as you have open. So if you're able to sustain 10, 20 tabs open, now you're counting as 10 or 20 likes 10 or 20 views and 10 or 20 live stream viewers. So this force multiplier was pretty cool because I think this is fascinating that he took the time to really poke at it. But I think there's some problems that came from it where once you had this many people on there, the YouTube algorithm kind of grabbed this and said, Hey, look, this is a popular live stream. Let's keep showing it to more people. And more people probably joined in the fun, which caused this live stream to get very, very big. And then the follow up video, the Spiff and Brit made about all of that also pointed out the fact that the ads were different for a single user. So you're not seeing the same ad for each tab open, you're seeing different ones. And I think YouTube said, Oh, the advertisers are going to see that this is a problem. So YouTube is starting to scramble and fix it. And whatever they did to fix it somehow has the side effect of flagging the content that we do as well more and more invalid traffic. I don't know this to be absolutely true. I'm just hoping some YouTube engineers who have undoubtedly spent lots of time examining how this Biff and Brit did things also see some of the videos creators are making talking about, Hey, we're here. Hey, these are problems we're seeing. We aren't just buying views. I think the channels that I'm seeing and especially the ones I directly know, they are not people who would do this. I'm not someone who would do this. I don't try to buy my way to the top with any of the videos. And it's just a big annoyance. And of course, if you're a smaller YouTuber and you rely heavily on that ad revenue and to extent, all of us have some dependency on it, but this is also why we have other sponsors for videos. This is a problem. Now I'm hoping I'm hoping YouTube fixes this and this problem gets resolved, because the only other answer you're going to get from a lot of YouTubers is more inserted ad spots where the creator themselves works out a deal and you see more mattress and food things that people have to insert into their video to cover the production cost of videos because YouTube has just cut out that whole section of revenue or as many of the small creators have noticed have lost, as I said, as much as 90% of their revenue. Leave your thoughts and comments down below. Maybe I'm right. Maybe I'm wrong. Either way, I want to add my voice to the choir of voices and creators that are talking about this just so we can raise some awareness and hopefully get this problem solved. All right, and thanks.


This is day one of spreading only football facts. Aisho Speed is doing more for footballing culture in America than Lionel Messi is doing at Inter Miami. I don't care that Messi has now made it cool to watch Inter Miami, because the second he leaves, so will all of those plastic fans. But Speed is getting them sucked in to football culture. He is putting front and center, the insanity that is the FIFA community, while getting people interested in football rivalries and presenting something that so many American kids would have never experienced without him. That is how you make real football fans. And that's why...