Step 2.  These grains turn into sugar even before you swallow. Amylase, which is an enzyme in the mouth, starts breaking down the starches into sugar in the mouth before you swallow them. Thus, by the time they reach your stomach, they have transformed into sugar, which is their true form. Step 3. Kick out vegetable oils. Canola, soybean, sunflower, you name it. They're inflammatory and not doing your health any favors. They have a very high ratio of omega 6 to omega 3, and they also oxidize very quickly when you cook with them and also in your body. Experts believe that they also contribute to the pathology that eventually leaves you with type 2 diabetes.


What's going on everybody welcome back to another predictions video and this time we are going through the women's Grand Slam champions for 2021 who is going to be the French open the Australian open the Wimbledon and the US Open champions for next year You guys voted for who you think is going to be the champions of next year and I've picked my picks as well Also, don't forget. We did do a video on the men's or we have done a video on the men's as well So go check that out if you haven't already also check out the ATP WCA top 10 predictions that we did about a week ago So go check those out as well and make your predictions down in the comments below Predict your ones for this as well down in the comments below. Okay, let's get straight into it Australian open. All right, so the Australian open. It's only about a month and a half away. It's very close Let's go through your predictions and this was the overwhelming favorite You guys have predicted that it's gonna be Naomi Osaka is gonna win the Australian open So she is the current US Open champion. She won the Australian open two years ago So she has won this event before so not a big surprise picking her again, but you've picked Naomi Osaka now Let's go through my pick. I have also picked Naomi Osaka. So no surprise there. I picked Osaka I think that there's players that could maybe upset her though. It's Mona Halep She made the semifinals at the Australian open this year Sophia Kennan defending champion Ash Barty the hometown favorite and world number one There's a lot of players you can make an argument for on Drescu's coming back Saber Lenka's played well Pliskov has got a new coach. There's so many players that could win this event Serena We haven't even mentioned her Serena Williams as well, but I've gone with Naomi Osaka just like you as well So we've both gone Osaka for the Australian open. Let's move on to the French open So let's go to the Roland Garros clay season Let's see who you picked for the women's title in 2021 Of course, Igor Sfiontek is the defending champion Ash Barty won it back in 2019 But you guys have predicted that the winner of the 2021 French open is going to be Simona Halep. So you picked Simona Halep I think she's the queen of clay and you guys also agree with me So you think that she is gonna win that event now Let's go through my predictions. Who do I think is gonna win the French open? I also think it's gonna be Simona Halep So it seems like the French open especially for the men and the women is kind of straightforward Of course, we've got to watch out for Sfiontek. She's gonna be defending champion Also Ash Barty didn't play the French open this year. She is kind of the pseudo defending champion as well So we've got to remember those two players and of course Serena is always in the draw Burton's is a good clay court player Pliskiw is not bad on the clay Mugaruta as well There's a lot of players we can make an argument for but we're both going with Halep Let's move on to the Wimbledon title speaking of Simona Halep defending champion of the Wimbledon title back in 2019 We didn't have Wimbledon this year, of course Let's go through your predictions for Wimbledon. So the Wimbledon title I haven't seen anybody play on the grass for almost two years once we get to Wimbledon This one's a tough one to pick just like it was with the men and you have picked Simona Halep to win so you think Simona Halep is gonna defend her title from back in 2019 She played a Halep a final against Serena Williams in the 2019 Wimbledon final I've gone a little bit different with this one. I've decided that I'm gonna pick Serena Williams I think she's gonna win at least one Grand Slam next year And I think Wimbledon's her best chance and I'm gonna stick with Serena Maybe not really a strategic pick. There's some big hitters in that tournament. Of course We've got Mugaruta. She's been there a champion before also Kovitiv is a champion there as well big hitters on Dresky on the grass I can't wait to see that but I'm gonna stick with Serena I think the goat is gonna get the record or equal the record next year And I think it's gonna be a Wimbledon a tournament that she loves to play And she's won a bunch of times as well So I think Serena's gonna win this Wimbledon in 2021. You guys think Halep? I think Serena.  Let's get a replay of the 2019 final And maybe be a bit closer this time because Halep destroyed Serena in that one. Now, let's go to the US Open It's a long way away. Let's try and predict the US Open title. All right So the US Open title for 2021. We've got the predictions ready to go And you have picked for the ladies event for the ladies US Open title for 2021 Naomi Osaka, so you have gone with the defending champion. She won it last year She's won it twice before. You're sticking with Osaka. You think she's gonna defend the title and win it again I've gone something a little different now Like I said with the men's video Random champions happen at the US Open and I'm gonna pick a random champion again another first time winner of a Grand Slam And I think it's gonna be Karolina Plishkova. I think with the new coach She's been to the final at the US Open before so she knows how to play well there I think with the new coach she could get over the edge and for some reason Naomi Osaka just struggles with the pressure of being a defending champion So I was reluctant to pick her as the defending champion because she just seems to struggle when she defends titles Remember Australia this year. She didn't play very well defending champion US Open as well last year Didn't play very well as the defending champion. So I'm not gonna pick Osaka based on that I'm picking Plishkova new champion and I think with the new coach she can get there Of course, let's do a recap. I have picked my winner for the Australian Open as Osaka your winner for the Australian Open Naomi Osaka with both pick Roland Garros champion to be Samona Halep Wimbledon champion. You've gone Halep I've gone Serena. So we'll see how that goes in about eight months And of course the US Open champion You've gone the defending champion Osaka and I've gone Karolina Plishkova to win that one and there you have it They are the picks for the 2021 season your picks my picks for the WTA Let's see how many we get right in about 12 months time. Let me know in the comments below Who's gonna make these champions? Who's gonna win these titles? Let me know your predictions because we all have different predictions Let me know for the Australian Open the French Open Wimbledon in the US Open Who are gonna be the champions for the WTA next year comments down below there are predictions Let's see how many we get right next year


John Crestani here and I'm going to be talking about some marketing that crossed the line here. There's three marketers in particular that the U.S. is going to be sending to jail because their ads were so effective but they went over the line. The first ad we're going to be looking at here is a fake COVID relief ad that was used to sell cars. The first part of this marketing if you look at the first letter they sent out these packages to people in the mail in their local area and these ads worked extremely well but they worked too well and they got them in extreme trouble because they didn't disclose their financial terms correctly. They faked COVID relief and amongst other items they had all sorts of problems here. Now the first part of this ad you'll see it looks like a real official government document. This is a highly effective tool that you can use in marketing is making your ads blend in with something that has a lot of credibility. You see this on TV sometimes where you think you're watching a public service announcement because it's got the same style but it's really an ad or you think you're watching a news show but it's really an infomercial or an ad. I did this in TV where my ad which is a 30 minute long TV infomercial it looks like you're watching the news but it's really an ad for my book. That isn't illegal but in this case faking that it is actually a medical relief okay it's very different. You can make your ads look like a government public service announcement somewhat or the news somewhat but not all the way and not medical relief and in this case these marketers went way over the line but they continue to go over the line. When you look they actually show a fake check. Now this is another tried and true marketing tactic that is very effective and I don't think there's anything illegal about sending what appears to be a fake check but when you are making your check look like it is a actual government stimulus for a deadly medical condition medical issues and the FTC do not work out well and this has an FTC case from the Federal Trade Commission which is the commission that governs marketers out here in the US they went over the line also you'll see that even their disclosures they don't let you know the actual financing all the numbers are fake they don't include any of the fees they're advertising what the product costs but it doesn't really cost that much these guys are just trying to get you to get on the car lot they're selling used cars they just want you to get on the lot so they can hard close you there they don't really care if they're advertising has any modicum of truth in it that's why these guys are getting sued by the US government and possibly taken to jail or their business shut down or find a lot of money this is a federal case the other thing you'll see that these marketers did again they must have had a very aggressive marketer working for them is they have a ton of facebook postings for cars now if you're selling used items in your local community that facebook's marketplace might be a good place for you to consider to market and i've actually i have another video where i teach people how to use the facebook marketplace to actually generate leads there's a lot of stuff you can do with facebook's traffic in the marketplace to make money as a marketer but in this case this was all false advertising you didn't actually have to put no money down there's all sorts of hidden fees and hidden charges and whenever you're messing with both the financial system you're lying about financial stuff and you're lying about especially lying about financial stuff where you're charging people money and hidden fees and all this stuff that's a big no-no with the federal trade commission and the other one is just anything medical related with false claims or really hijacking the trust and credibility of the u.s. government will land you in a lot of problems very fast so this marketer even though it seems like they have some good marketers on their team that know the right stuff they're messing around in the wrong field which is with hidden fees and fake medical relief big no-no's let's go on to the next one now this next ad is from a very large company and this is a very large ftc case that is being preceded which is for Lending Club which is a lead generation company for mortgage officers and mortgage and refinance companies that is for those of you not in the u.s. that means home loans so this is a company that is generating interested people's information and then they're selling their data to actual home loan companies we're talking big money here hundreds and hundreds of thousands if not millions of dollars that each of these mortgages or refinances are worth so it's a big chunk of change and in this case they're just trying to get high conversion rates i mean that's how these companies work when you're generating leads you want the highest conversion rate possible but these marketers cross the line because they claim there's no hidden fees but once again they're running into trouble because there are hidden fees and lending club is failing to disclose them or they disclose them in fine print way too far down and when you're dealing with credit and complex financial instruments and you're not disclosing things properly that will land you in a lot of trouble with the government as is the case here and furthermore they are saying in order to get people to give them all their information so they can sell the leads for more they're saying everybody qualifies for a loan when in reality it's just marketing it's there there is nothing on the website that's actually checking if these folks are qualifying for this actual loan it's just a marketing tactic to get more information so they can sell more data and that's a big no no i used to market mortgages myself that was the last job before i became a full-time internet marketer and i was marketing mortgages and there's a lot of regulations when you're dealing with large amounts of money and complex financial instruments that need to be abided by and you can't make wrong steps when you're doing that now after i got out of marketing mortgages and i started working for myself i was marketing an investment company and once again there's a lot of regulations there's a lot of money there right because when you're dealing with the financial system there's just endless streams of money changing hands and really all you need to do is just poke your head up stick your hand in and just catch a few dollars because there's so much money flowing through loans and investments it's insane and i made almost over half a million dollars marketing the investment company as an affiliate and that was the first year or two of my career as an affiliate marketer now if you want to learn more about affiliate marketing and the business i did to or and i still do to make a lot of money and how i built my wealth and i now own two acres huge property in malibu i built a really incredible life and i'm really proud of it you can go in the links in the description you can see where you can sign up for a free training for super affiliate system which is my training program i've trained tens of thousands of folks to earn their first dollars online and become affiliate marketers we do everything the legal and compliant ways i don't teach these fast methods that will land you in jail because marketing is a regulated thing around the world and you just can't make false claims in this case the lending club was trying to make a quick buck at the expense of people really now this last case which is being investigated and prosecuted by the u.s government the federal trade commission is for another large company named tap joy and they partner with mobile app companies to show ads and sell virtual rewards and whatnot but the problem here is that they did not disclose hidden fees once again the government and governments around the world hate when you go out and you market products and you play around with people's finances and you're not upfront about what something costs and how many payments you will receive your credit card will get dinged in this case this is somewhat similar to lending club whereas tap joy is an intermediary for a lot of other different companies they say buy this product to get virtual rewards that you can use in the game and they've partnered with a bunch of other companies dozens or hundreds of other companies to create signups now this can be an effective way to create signups but the problem here is is that tap joy didn't look into the companies that were advertising on their platform and many of them had hidden fees and the problem is the government doesn't care that even if tap joy you know claims to be just an intermediary tap joy says oh no it's not you know it's these other companies that have the hidden fees we just allow them to advertise on our platform and people kind of sign up it doesn't matter in the government's case because in their mind tap joy was enabling these companies to actually defraud the public and that doesn't fly in same thing in lending club lending club is just generating leads for these other home loan companies hey it wasn't us right we're just an intermediary doesn't really fly because they are expected to do their due diligence and as a marketer it's important to know this because even as a marketer you could say oh i'm just marketing the company but it's up to you to know okay did somebody do their due diligence here you know that's why i like to work with long-term affiliate networks that are established and do due diligence for me so i don't have to go through all those pains and that's why i use clickbank and digi store because those are long-standing affiliate networks clickbank is possibly the oldest affiliate network online they were created in 1998 and they vet all their companies and they make them go through compliance checks before they allow you to market products i've made millions of dollars through clickbank i have an affiliate program on clickbank called a super affiliate system and you can earn five hundred dollars for every sale on there great training program link is in the description and i hope you enjoyed this video kind of showing another side of marketing of what you don't want to do you don't want to get in trouble you don't want to do these bad things when you're starting your business so make sure you have all the information you need to make sure you're going the straight and narrow path you want to earn your money and not have to look behind you to see if you're going to keep it you want to sleep well at night so make sure when you start marketing when you start your home-based business whether that's you know learning affiliate marketing from me or whether that's learning how to sell on amazon from somebody else or learning how to drop ship by using Shopify whatever you do i do encourage you to get training because there are so many ins and outs of this world nowadays and there's a lot of regulation and get your training from somebody who actually addresses some of these topics because i see a lot of marketers i see a lot of gurus and trainers that really talk zero about the legal ramifications of selling products online and the reality is that we exist in a world where there are rules where there are laws and in order to be a good citizen just a good person you should abide by them so hope you enjoyed this video leave a thumbs up give me a like in the comments hit that subscribe and notification bell any questions you have about any of this or even if you enjoyed the video or if you hate the video or you think i should discuss different topics about these cases let me know in the comments below i really value all your feedback and i read your comments and thank you for participating in learning to create your own success so i'll see you in my next video bye


Good morning and welcome to EGU22, the annual meeting of the European Geosciences Union. As many of you already know, this is the Union's first hybrid general assembly, where we are bringing back our on-site experience for those joining us here in person, while at the same time introducing new concepts from the last couple of years to include our virtual attendees as much as possible. This year we've had more than 12,000 abstracts submitted to EGU's meeting and during the press conferences we'd like to highlight some of the most unique ones which, as you'll soon see, have impacts on local communities, industries, ecosystems and the global environment. I'm Jillian D'Souza, EGU's media and communications officer and I'll be hosting this week's press conferences. Each press conference will have time for speakers to make their presentations followed by a question and answer period at the end.  If for some reason you experience technical difficulties, you can try to rejoin the session or look for more information on the press conference section of the media.egu.eu page. A last couple of things to note, please save all of your questions for after the speakers have finished presenting. During the Q&A period, we will take questions from journalists both in the room and online. If you're in the room, please raise your hand so we can pass a microphone over to you. If you are joining us virtually, please use the hand-raising function of the Zoom platform so we can come to you for your question. If you prefer to type your question instead, that's fine. Feel free to do so in the chat. Okay, so I'm now going to go ahead and introduce our panelists to make for faster transitions between them. So our speakers today are Eleni Dragotsi and the special account division ELKE, National Observatory of Athens Institute for Environmental Research and Development. We have Maria P. Kohler from the Institute of Mountain Risk Engineering, University of Natural Resources and Life Sciences Vienna, and Sylvia Nunes from the Instituto Dom Luiz, Faculty of Sciences, University of Lisbon, Portugal. So from three of our speakers today, two of them will be joining us virtually, that is Eleni and Sylvia, while we have Maria present here in person. Okay, so I will now hand over things to them to share the exciting findings with us. Thank you for joining us today. Good morning to everybody. My name is Eleni Dragotsi and I'm going to present you our study entitled Operational Estimation of Daily Dead Fill Moisture Condits, the case of the risk. Sorry. The research presented today was conducted in the frame of the Project Limax National Network. Over the past years, the Mediterranean areas are experiencing more frequent and more severe wildfires. In this context, the estimation of dead fuel moisture content has become an integral part of wildfire management, since it provides valuable information for the plummability steps of the vegetation. At this point, it should be clarified that the dead fuel moisture content refers to the moisture remaining in dead plants, including annual grass, etc. From now on, we will use the term TSMC to refer to the dead fuel moisture content. Most of the large-scale wildfire in Greece usually coincides with periods of high dryness in forest and woodland ecosystems. During drought, the risk of fire significantly increases, mainly due to the reduction of fuel humidity. Therefore, the estimation of fuel moisture content is becoming an increasingly important component in Greek early warning systems. This need motivated us to develop a daily mapping service. To do so, we investigate the effectiveness of a recently developed dead fuel moisture content model in the light of operational use. To fulfill the objectives of this study, we tested and compared two existing approaches in estimating daily DSMC. In the first approach, we calculated daily DSMC using remote-sensing data, using the no-lamps DSMC model at national and regional level. In the second approach, we produced counter-level DSMC maps from weather station data using the same model as well. For the purposes of the analysis, we used more these remote-sensing data, as well as meteorological data obtained by the dense network of automated weather stations operated by the meteor unit at the National Observatory of Athens in Greece. Here, we can see two examples of satellite-derived modest DSMC maps. On the left figure, you can see the satellite-derived DSMC map at regional level, while on the right figure, you can see the counter-level produced modest DSMC map. Here, we can see an example of a DSMC map derived from weather station data. The date of production is the date of the most catastrophic wildfire events in Greece, which were held in July 2018. The areas with the lowest death fuel moisture content values and subsequently the areas with the highest flammability levels are depicted with red colors. Due to a lack of DSMC field measurement, the validation of the produced maps was possible only in the case of the satellite-derived maps. The results from the implementation of the two previously mentioned approaches indicated that at present only the weather station-based approach meets the requirements for operational DSMC mapping. To this end, the daily DSMC mapping service that operates today by the meteor unit at the National Observatory of Athens uses weather station data to produce free access daily DSMC maps. In Figure 8, here in the last figure, you can see an example from the operational estimation of daily death fuel moisture content. Thank you very much for your attention. National Natural Resources and Life Sciences, we say actually BOKU in Austria, so this is in Vienna, but still we're going to stay in Greece, so it was nice to see the previous presentation. Me and my colleagues from the BOKU went from the University of Athens. We are working, since I don't remember when, with vulnerability. Vulnerability to make it very simple is just all these characteristics that make something susceptible to natural hazards, let's say it like that. So we are looking at houses and buildings, infrastructure, and we have done some work on tsunamis and on dynamic flooding in mountain areas in the past. But there was this, exactly this event that you see here in the photo. It was the event that also the previous speaker talked about. This is the fire in July 2019 in Mati. This is 30 kilometers away from the Greek capital from Athens. What was special about this fire was that first of all it created loads of damage and loads of costs and of course environmental damages as well. But the most important thing which was traumatic for the whole country was that we lost 102 people. So that's an amount of casualties in a European capital, more or less. It is dramatic. So we started thinking, okay, hold on a minute, let's have a look at the place. This is where Mati is. This is Greece, so not all of it, but you know, like Peloponnes, you can recognize, oops, I just have to go back, excuse me. Okay, this is the area. It's a typical wildland urban interface area, we say, where forests and buildings meet. And the fire started five kilometers away on the mountain. Somebody started burning leaves on a very dry and hot day. And this happened around noon and in the evening we had 100 dead people. So what is going on was that dry weather, high temperature and winds helped the fire to propagate towards the settlement. People didn't have any plan. The settlement was very wildly made. It wasn't planned at all. And they got trapped in there so they couldn't escape. Some of them did though. And I will tell you about it in a minute. First of all, our colleagues from Athens were lucky and unlucky enough to be there the day after to collect data from the buildings. So what happened was we see some buildings from the area exactly the next morning. You see that the roofs are burned, so in the top three photos, the roofs are burned and all these features of the building like shutters or fences or all these things that are not made from concrete, for example, let's say, were burned. Whereas other buildings weren't really damaged at all, although the vegetation around them was burned. So three questions were generated in our heads. Are all the buildings equally affected by a wildfire? The second question is, can buildings act as shelters? So in Australia they have what they mean, what they name shelter in place. So people instead of evacuating, they stay at home. But of course, this home has to fulfill some standards. And they don't have to evacuate where also it's not safe because evacuation is also not a very easy thing to do sometimes. And the third question is what can we do to reduce this physical vulnerability? So if we look at the buildings very closely, maybe we can find out that there are some very, very easy and cheap things to do to change in the building. And then we reduce costs. And of course, the most important casualities. So let's go to the next one. This is what we did more or less. I don't want to bother you with very, very complicated figures. But what we did was we went to the literature. We had to look to see what kind of characteristics are they considered as to have some sort of relation to the vulnerability of buildings. So we saw that there are many, many different things like the shutters, as I told you, the material of the building, the shape and the material of the roof, of course, the surrounding vegetation, the type of the vegetation, the distance from the building and all these things. Then we collected all this information. Our colleagues from Athens collected all this information for each building in the area. And these were around 420 buildings. And then we used statistical methods to see which of these characteristics is relevant, because not all of them were, and which one is more relevant than the other. So in this way, we could weight them and put them all together in an index. So each building could have its own wildfire vulnerability index at the end. What we did was, this is again a complicated, maybe a figure, but I will tell you what it means. It means that not all the indicators, all these characteristics that we collected for every building seem to be relevant. But some of them were. And that was that were roof material, structural type, the slope of the terrain where the building was located, the vegetation around it, the possibility for leaf accumulation on the roof, because this is also something that burns. And if the roof has such a shape or such a material that doesn't really leave the leaves go, they accumulate there and they can, they are combustible material. The material of the shutters can be, in Greece it's very often the shutters are from wood, are made of wood. Then the main ground covering and the roof type. So these were the indicators that we can more or less say that they are relevant and they have to be included in an index. Of course, we don't stop there. We would, we have some next steps planned. We would like to validate our index in another area or collect even more events, more information from new events. You see here, these are photos that I made from my car last summer in Greece. We had huge fires in the whole country, but especially on the island of Evia. And if we could get hold on this data, for example, we could provide something more reliable, maybe, or reconsider some indicators. So that would be a wish. Then it would be nice to visualize our results on a map. And finally, to include more in the intensity of the fire itself in the index. And the most important is to transfer this index in other countries. As we have seen also in Austria last October, we had a huge fire not very far away from Vienna. It didn't threaten any buildings, but it was something new for us here. It burned a large area from 100 hectares. And of course, it has become now a very hot topic also for the authorities and for the ministries and for the policymakers. So the whole thing is very relevant, I would say. Thank you very much. Thank you, Maria. We will now move to our last speaker's presentation. If we can have Sylvia on screen, please. I'm so sorry. I think you are seeing everything, right? Hi. Yes, Sylvia, you're good to go. Sorry. Good morning. So my name is Sylvia Nunch. And I'm going to talk a little bit about what we have been doing in Lisbon University with the University of Rio de Janeiro. And we wanted to create an early warning system for fire danger for the Brazilian Pantanal. And so for starters, one thing that we need to understand is that in order to have a wildfire, we need to have three main ingredients. So we need to have an ignition, we need to have vegetation, and we need to have the neurological conditions, the atmosphere in order to ignition to become a wildfire. So in this trend of the thing that is most interesting to us is the neurological conditions, because it's the one thing that is completely predictable. But how can we predict the neurological conditions? So one thing that is widely used is a thing called fire weather index. And this index is basically created and calibrated for the Canadian forests. And it's a combination on several factors as well. Rain, temperature, wind intensity and relative humidity. So they compile everything in one index and this index gives us a number. However, and 40, for example, is a number that it can be considered as high. However, it's just a number. So what is the real meaning of this number? So for example, I'm going to give you three examples. As I said, this was calibrated for the Canadian forests. So in our world, we don't have only Canadian forests, we have different types of biodiversity. And for example, if we imagine that we have this number for these three different areas, if we have for the on your left side, the first image, when you have a desert area that we don't have anything to burn, of course, even though it's a high number of fire weather index is hydrological danger, it's nothing to burn. So the classes of fire danger need to be low. In the meanwhile, on the middle, you have a pasture. So if the ignition starts, it's going to burn. However, even though it's going to burn, it's going to be a burn that probably firefighters could control it quickly. And it's not going to be as intense as for example, your right image when you have a forest, when you have more biomass, it will be a more intense and more dangerous fire. So in here, we have three different levels of fire danger with the same value for the meteorological fire index. So how can we calibrate this number in order to have classes of fire danger? So what we use is satellite information. And we have a lot of good data that's come from satellite, but in this case, we use the intensity of wildfires, the fire radiative power.  And if we have a more intense fire, so we are going to have a more difficult fire to firefights and it's going to be more severe and it's going to have larger bunderias. So for our study, we use the region of Fentanyl divided by nine hydrological areas that you have on your right side. And we've done a model to every single one of those areas. And the model, one of the results is this cumulative density function that is a fancy name just to say that is a probability that the intensity will take a value that is less or equal than a certain threshold. Just to a better understanding, I'm going to explain a little bit closer to the plot. So in the curves, what you have is the cumulative density function for different levels of our meteorological danger. So in blue, you have the function for her lowest values of FWI. And in red, you have highest values of FWI. On your x-axis, you have from the left to your right an increase of the fire intensity. So let's imagine that we have a threshold of 100 megawatts. So if we look to the values of due to the distribution of the FWI, the meteorological danger test, the lowest values, if you look to the graphic, we see that's probably will have 0.8. So 80% of the probability of having a wildfire that is lower than 100 megawatts. So we'll have 20% probability of a larger wildfire than 100 megawatts. If you look to the other hands for the highest values of FWI, we have the complete contrary. So we have 40% of having a wildfire that is below 100 megawatts. So 60% of chances to have a fire that is going to be above that 100 megawatts threshold. So we pass from a probability of having 20% to 60% using this FWI, this meteorological danger, in our models. So with this, we created our classes of fire dangers. So we chose a threshold 100 megawatts and then we decided which probability, knowing the FWI, we would use for each class. So we have low danger, moderate, high, very high and extreme. So for the results, this was the results for the calibration of the model. The most important part is on your left side, you have the classes of fire intensity and above you have the classes of fire danger. And the most interesting part is the fact that for classes of fire intensity, the highest fire intensity, you have a shift for the very high, very high and extreme classes. So it's a very good indicator. For a better understanding, we've used this in a graphical mode. So this was for 2020, one of the most severe wildfire seasons in the region, Pantanal. And in here what you have in red, sorry, yellow, you have high classes, orange, very high and red extreme.  And what you can see in here is that the fire, the more intensive fire seems to follow the more extreme classes. For the future and what we want to do now is to create a prototype. So is to create a page, a web page, where this information can be presented for the next five days. And we've done this already for Portugal. And this is one example on your right side. For 2017, one of the most severe wildfire seasons for Portugal. And just for you to understand and see the redness, the most darkest areas are the ones with extreme fire danger. And the flames are the fires that were active at that time, so by satellites, so the most intensive fires. And they seem to follow very well the most extreme classes. So this is useful for, for example, civil protection or policemakers or firefighters in order to understand where in the next few days we can allocate our means in order to protect our areas. So for example, if you have a northern part that has more danger than the south, maybe you need to allocate more means like the airplanes, like drugs, like firefighters in that area, or even to prevent the ignition. So we need to go and put more police make police and so on protecting and 17 is okay. So this was my presentation. Thank you so much. And if you have any question, please. Thank you to all of our speakers. I think we can all agree that those were some interesting collections of research. And as you've probably already know, this was all under the press conference theme this morning of communities vulnerable to wildfires find ways to prepare. So now we can move on to the last part of this morning's press conference where we will have the Q&A round. And during this section, we will take questions, both from journalists in the room and those online. So I would like to officially open the floor now for questions. Just to recap, if you are in the room and if you have a question, please raise your hand. And if you are joining us virtually, please either use the hand raising option on zoom, or you can simply type in your question on the chat. Okay, so I think we have a question that's come in from online. I will ask our press assistant, Wong to please ask the question. Yeah, so from the online audience, we have one question from Nicholas. And the question is for Maria, what are the main similarity and differences in the situation between Greece and Austria? Can you give an example on how you are trying to transfer the research from Greece to applied region? Thank you. Very good question. Actually, the two countries have many, many differences.  But then again, it's not that much different anymore. So Austria experiences also longer periods of dry days and high temperatures. So in this respect, the two countries have some similarities or they will have in the future. The buildings look very much different. This is also a problem. But on the other hand, Austria has a very large amount of buildings in the wildfire urban interface. So in this area where our buildings meet the vegetation. How we could transfer it? First of all, we could transfer it like that and see if it works and seeing the future if it really made sense. This would be the most simplistic thing to do. The second thing is that in Greece, we made the index based on damage data that we already have. We don't have this in Austria. But what we have is we have experts and we have people that know many things and we could put them all together and try to make based on expert judgment an index. Or to make sure that we document the event so good that we will have this information that we need for an index in the future. Thank you so much, Maria. Do we have any other questions? Okay. I'm just going to pass the mic. Thank you. Those really interesting presentations. So my question is for Sylvia. So you mentioned in your talk that these sort of thresholds of danger perform quite well. You showed where all the fires are breaking out. Have you done any assessment of how much better your danger thresholds perform compared to the fire weather index that's traditionally used? We have some experience with national authorities in here that use that FWI just as a threshold. And for some regions, and Portugal is a small country, however, we have a lot of different places with different biodiversity. So we have some areas that the model performed a lot better than others, just because it has that information, the historical information. So for some areas, it's almost the same because the FWI, it works well if you have the fuel, if you have the ignitions. However, for the areas that we don't have as much fuel, as much as much ignitions, it works better for our understanding. Thank you. Do we have other questions that are from the room or online? Okay, we have a question. Just going to hand you the mic. I have a question for Eleni. So in your approach, you use satellite data, right? So I wonder if Z-data is freely available, so in the way that are the researcher or we can apply for another reason in the country? Yes, there is a modest land safety data that we use in the model. It's free access, and so we can produce the daily DSMS maps every day. Is that your question? Yeah, yeah, thank you. Thank you. Any more questions? Okay, what do you? I have another question for Maria. So are you planning to apply the same assessment for the let's say social vulnerability, for example, the population under five years old or like over a certain threshold, where they have like difficulty to move during the event? Well, I'm afraid then I would end somebody else's field and I wouldn't like to do that. So there are many people that work on social vulnerability for different types of natural hazards, and I guess there will be also for wildfire. What I see, what is for me very interesting is not the social vulnerability, but the institutional vulnerability, to see how people are organized, what kind of legislations they have, how much corruption is it involved. The land use planning, or for example, the place I was talking about, was an illegal settlement in the 60s. In the meantime, it became legal, but still there is no planning there. So there were people that they had occupied the coast, which normally in Greece is not allowed to do, and they had put the fence there so that they have their own beach. What happened was the people wanted to evacuate, and they couldn't, it was the evening also came in the meantime, so it was very dark and very smoky, and they couldn't find their way to the beach because of these things. So there are also other things, it's not only the social vulnerability as such, but also the institutional setting of countries, I would say, that also makes some things even worse than they are. Thank you. Thank you. We have one more question from our in-person audience. Hello, so I would have a question for Silvia. So I was wondering, I would be curious whether you could test your approach already with the people from the government, like that you already know if it would be helpful for them as well. In Portugal we have direct contact with civil protection and some national authorities, and actually with some companies that have some lands that they want to protect. And these kind of models were so good for them, and they use it so often, that we actually started to do this to other areas like Mozambique, where one of the companies has some lands too. So just to give you an example of how this can be used and was used, we know that in 2018 we had an area from the south of Portugal in Monchique that presented higher values of danger for that day in July, in the end of July, that were higher than the rest of the country. And the civil protection started to put some more airplanes in that area and more trucks and be prepared. Unfortunately an ignition started and it became a very large wildfire, however they were there. And I think they used this information, they told us that they used this. So yes, they say that it's very useful and we are trying to do this for Pantanal too, and probably to other areas then. Thank you, and thank you for the question. Thank you everyone. As we are now nearing the close of our press conference time slot, I would just like to say that we don't have any other questions coming in both virtually and in person. So I think we can wrap up today's session. Thank you so much for joining us for this briefing. If you're struggling to connect with any of the speakers for interviews or comments after today's press conference, please drop me an email at media at egu.eu. We have five more press conferences lined up today and the day after. So please be sure to visit the media.egu.eu page for more information. Thank you once again. Thanks.


Okay, good afternoon everyone. And we are excited to start our sharing today. We would like to share our experience about how we explore adopting reinforced learnings in Kubernetes this game. My name is Haosong, and I will join with my colleague, Xu Ming. We are both engineer in the engineering infrastructure at Shopee. Due to the V-side issue, sorry for that. My colleague, Xu Ming, will share remotely instead of on-site. Before we get started, I would like to give a brief introduction about our company. We are from Shopee, which is an e-commerce company operating in several markets across the world. Today, we are the leading e-commerce platform in Southeast Asia, Taiwan, and Brazil. We are also the number one shopping app in these markets by average month active users, as well as a total time spending app. In Shopee, we have used Kubernetes to manage and orchestrate large numbers of ports that are powered in the backend systems behind Shopee. Today, we have over 100,000 ports run across tens of thousands of nodes distributed across multiple data centers worldwide. And we expect these numbers to continue growing as a company grows in these years to come. For today's talk, Xu Ming will introduce the issues we encountered in Shopee scenarios and how we use the traditional way to address them. Then I will continue to share how we explore reinforced learning to address the problems we are encountered. At the end, I will also introduce the disadvantage of reinforced learnings we found when we lower it to production and how we plan to address in the future. Now, let me pass to Xu Ming to share his part. Okay, thank you, Haozong. As we all know, Kubernetes is a side post to certain nodes in a cluster for the Kubernetes scheduler. The scheduler's primary objective is to optimize resource visualization and ensure that the application runs smoothly and efficiently. Kubernetes scheduling is composed of two main phases. Featuring phase and scoring. In the filtering phase, Kubernetes eliminates nodes that are not suitable for running a pod. It will filter out nodes that cannot provide enough resources for a pod request. In the scoring phase, Kubernetes assigns a scope to each remaining nodes based on various criteria, such as resource utilization, core affinity, and node affinity. Kubernetes scheduling enables work load distribution, load balancing, and forage tolerates. It plays a vital role in optimizing resource allocation. But the result is a runtime optimal decision based on the current cluster resource technology. It doesn't adapt to changes in the cluster if a pod is running. After running a Kubernetes cluster in live environment for some time, we found that changes in the cluster resource status could cause the original efficient and optimized pod distribution to become less than idle. This can lead to uneven work load distribution and inefficient resource usage. The reasons might include nodes are underutilized or overutilized, new nodes are added to clusters, nodes hardware specs, situations have changed, or have no affinity requirements, such as themes or labels have changed and the original scheduling decisions are no longer appropriate for certain nodes. Different applications times adopt different oversold strategies and may even adjust on demand. Some ports managed by custom controller cannot be rated arbitrarily. The scheduler project tries to solve the above problems. It provides an automated system for running rescheduling. The working mechanism of the scheduler primary resource around a set of processes that define when and how to reschedule ports within the cluster. Here is the basic workflow of the scheduler. First, the scheduler begins by inspecting the current state of the cluster, including the resource usage of nodes as well as the distribution of ports across them. Then based on predefined policies, the scheduler determines which ports should be rescheduled for instance, if a node is over-node and there is a policy to handle over-node, no over-node nodes, the scheduler will mark some ports on the node for rescheduling. Once ports have been marked for rescheduling, the scheduler issues a ration command for the Kubernetes API. The Kubernetes scheduler finds new nodes for the evicted ports based on the current state of the cluster and rescheduling process. Finally, the scheduler is typically set to run at regular intervals to ensure that the cluster continuously maintains an optimal state. It is important to know that the scheduler does not direct move a port from one node to another, but rather it restores the port. Ports are still scheduled by Kubernetes scheduler. In practice, we found the resource utilization of most clusters have been significant improvements, but the scheduler was not working as well as we expected. The major deficiencies were on compressed configuration, the powerful capabilities of the scheduler depend on a series of compressed process configurations. This could post configuration and management challenges for arbitrators, especially in large-scale and compressed environments. Two, some ports are eroded to frequency. The same specific ports are likely to be eroded irreparately, and they may even return to their original nodes after eviction. This is because the decision on whether the node capacity is balanced just based on the Ports request without considering the actual workload of the nodes. Three, inability to handle certain times of Ports. There are certain times of Ports, such as demon set, clone set, emplacement Ports that the scheduler cannot handle. Four, potential for service in Russians. The scheduler eroded Ports may need to temporary service in Russians, especially if Ports de-russian budgets are not currently confused. We need to find new ways to further improve resource utilization while maintaining capability. Okay, that my clinic also introduced how we try to solve this problem through the reinforcement learning method. Thank you. Thank you, Xiuming, for your introduction to about disk-garing in short piece scenarios. While loop-based disk-garrier achieve outstanding results in these scenarios, addressing our specific problem effectively, we are curious about potential more efficient methods. With the rise of AI, we are exploring whether machine learning technologies can enhance scheduling efficiency. Reinforced learning in particular has been widespread applications in scheduling research in recent years. But what is reinforced learning and why is suitable for scaling? If we categorize scaling machine learning algorithms by objective, there are four into two main groups, prediction tasks and decision-making tasks. Prediction tasks includes, for example, use the AI to determine if images contain a human face, or large language model like chat GPT predict the next token based on the previous input. Decision-making tasks, on the other hand, involve training as an agent to autonomously making decisions to achieve a goal, such as playing games. This diagram illustrates how reinforced learnings make decisions and take actions. In this framework, we refer to the training target as an agent. The agent learns policies during training. It observes the state and the environments, taking action based on the observation, trigger environment changes, and receive new states and rewards in response. The agent continually improves its policies, taking actions, then alter the environments and garner new rewards. This process repeats until the agent maximizes rewards. We will encounter several terminology comments in reinforced learning, such as agent, state, environments, action, and reward. This test has specific meanings in the context, which we will explore in later sliders. In reinforced learning and popular organizations by training targets, Q-learning utilize a Q-table to maintain state action transitions. This is effective for load dimension data. However, with high-dimension data, tabular methods falter, with the rise of deep learning, in past decades, there have been a shift towards integrate it with reinforced learnings, deep Q-learning. For instance, in neural network to learn and store policies, a sufficiently deep and large-scale neural network can model compressed and data relationships, even with luminous dimensions. Reinforced learning has proved he's successful in various applications over the year. The most notable work is AbraGo, developed by Google Demine, which defeat tall human goal players and showcase the amazing capabilities of AI. Other famous application is auto-parallel systems for the innate training tasks. Brands like Tesla utilize reinforced learnings to control speed and steering, adapt to diverse situations. Given its power-improved success across industry, can reinforced learnings be applied to scaling challenges? After we've received recent research, the answer is definitely yes. Scaling problems can align well with the Markov decision process, or area where reinforced learnings good. Siphenicon research, such as Google Demite, published eight years ago, has adopted the reinforced learnings for compressed scheduling tasks like multiple resource diamond packing, achieved state-of-the-art results compared to the previous heuristic algorithms. Virus-academic institutions has published numerous papers in this field, flowing to different categories based on the methods of implementation. One approach is to use the agent, train as the reinforced learning, as a scheduler to derive resource allocation and management. Another approach involves use the agent as a stretch selector, choosing the most appropriate loop-based scheduler based on the current situation. In our case, we adopt the first method, enable the scheduler to handle multiple-dimension data at the same time. Now that we have deceived our approach, let's discuss how to implement the incubinities discarding. The process may struggle with only four steps to apply reinforced learnings to Kubernetes discarding. Defy the problem, implement the reinforced learning of a reason, train the agent, and then deploy it to production. However, there's a cache. We find there are no existing work on applying reinforced learnings, specifically in Kubernetes discarding. While there are many studies on using reinforced learnings in general scheduling problems, none are literally applicable to Kubernetes or discarding in particular. Thus, we have to develop our solutions from scratch without relying on the previous work. The first step is proven definition. It's crucial to clearly understand the problems we are addressing. As this will significantly influence all our reward functions designed in later stage. In the context of Shopee, we have identified our objective is to achieve balance across the cluster. The next step is to qualify what constitutes balance or imbalance across the clusters. To measure the imbalance of the worker loads across the cluster, we use the variance of the load. Our higher variance indicate a greater imbalance. The ideal variance is zero, signifying even load distribution. We use the changes in variance as a reward signal during training. If the variance decays after the agent's action, you receive a positive reward, reflecting a more balanced state than before. Since the agent learned to maximize cumulative rewards, this guide towards policy that enhance the cluster balance. The reinforced learning divide the schedule, the divide the action and observation space is precisely is essential. The action space includes all potential actions, the agent may take. In our agent discarding scenarios, we define it as a pair comprising of a container and a worker load. The observation space include all possible states the agent may observe. For discarding, this means it being a way of resource allocation and usage across the cluster. We represent these user metrics that includes each worker nodes allocation and usage states. The allocation and usage states for each worker load also can be further detailed to include every container's resource allocations and usage. This comprehensive observation allow our agents to accurately assess the state of each containers in each node. Enable more precise actions based on its observations. Having divide the essential components of a reinforced learning program, we can now begin our implementation. There are several mature libraries available for this purpose, such as Base 3. We will utilize two popular libraries widely used in reinforced learning. The first is skin created by OpenAI, which we will use to implement a customer environment that interacts with the Kubernetes cluster. The second is Ray IL-LIP, which provides a lot of ready-to-use reinforced learning operations. Recall the concepts of the environments in reinforced learning. We need environments that interact with the agent. From the agent, they learn policies. But how does Kubernetes cluster interact with the agent to facilitate this interaction? We use the GIN to implement an adapter, creating a bridge between the agent and the Kubernetes cluster. When creating a customer environment in GIN, key interface need dividing. The most critical is the stair method. The stair method receives actions from the agent, then return the new states and rewards after applying the action of the environments. This is where the most of the logic is implemented. The other method, research, and render are used for environment utilization and visualizing the agent's states. After implementation of these customer environments, we proceed to implement these reinforced algorithms used Ray IL-LIP. The first, we register our customer environments, then we select appropriate training algorithms with specific configurations and hyperparameters. We also define the process for training the agent with the true reinforced learning algorithm. You may notice that we use PPO to training our agent. PPO, or possible policies optimization, is created by OpenAI as well. TripGPT also employs PPO in its tightening, known as reinforced learnings from human feedback to better align the response with human preference. We explore the other reinforced learning algorithms like SAC, but PPO's training process prove more stable and yet better results more easily. PPO also uses an actor-critic architecture, means it training two neural network. The policy net defines actions based on the given state, while the very net is teammate accumulated the various from the state and the action, provide feedback into the policy net. These neural are trained together, each will improve the other. This method balance the advantage of policy net and very, very methods, leading to better convergence and the ability to learn stochastic policies. Compared to other actor-critic algorithms, PPO introduced clipping mechanisms and copper liberal divergence to prevent overly large policies updates. These mechanisms balance its progression and its politician, resulting in better performance and stability. PPO also use generalized advantage estimations to estimate the various balance bias and variance, leading to more effective learning and more stable policy updates. For training, we use Cooper-Ray, a Kubernetes operator that simply by deploying a manager-Ray in Kubernetes. Cooper-Ray can distribute Ray's worker loadouts across multiple Kubernetes posts. Different posts are trained in a distributed view, concurrent manners with very hyperparameters. Upon completion, we select the best track points from the training histories. The training initial results looks good with the training converging over episode. However, some agents still exhibit slightly overfutings or underfutings toward to the end. So how we adjust it? One approach is to continue fighting the hyperparameters of the PPO or the reasons to balance the exploration and its politician. Hyperparameters like learning rate, clip parameters, batch size, and entropy co-efficiencies all have different impacts on the training outcomes. Ray's official documents provide more details on these effects. A useful trick is to leverage it to Ray turn API, which allow us to great research the exploration within a range of hyperparameters and combinations. Within Cooper-Ray, the process is distributed across multiple Kubernetes port for concurrent trainings enable more efficiency and easier identifications of the best hyperparameters. In addition to the hyperparameters turning, we also can consider changing the backbone network of our reinforced learning model. Our PPO implementation currently use a fully connected network, but other research also experiment with the conversion neural network, low shorter memory networks, or transformer. The truth depends on the nature of the operation space. For instance, if the input is the images and conversion neural network may be more appropriate. To exploit the convergence, we also can adjust the definitions of our rewards and action space. This to enable more efficient policies is pro-lations by the agent. For instance, pulling the invalidations can prevent the agent from learning incorrect policies. Additionally, including penalties into the reward systems can deter the agent from learning undesirable behaviors. During the training, we also noticed interlating with the actual Kubernetes cluster and waiting for matches to update from the monitoring systems. For each episode, it was too slow. To assert this, we developed a Kubernetes scheduler simulator. This simulator goes through the scaling process without actually in launch port, similar to the K-Work. This approach doesn't require setting up a full-size cluster and still allow us to simulate the entire process for policy learning. You also reduce the risk of the agent disrupting the Kubernetes cluster during training. Now that since we don't create a real port for the simulator, we use random usage data flowing on normal distributions as part of the observation space. This prevents the training on unstable agent with empty data from the monitoring system. Finally, we obtain a stable checkpoints that meet our requirements without over-futing or under-futing. Yeah, here is an example demonstrating the disk scaling process during this checkpoint. You can see the cluster usage distributions become more balanced as the disk scaling process become to evade a pause. So it's just too early. So we consider deploying to production. However, our SRA teams raise several concerns about the disk carrier and reject the low-out. Their concerns is quite valid. For example, they point out the neural network is a breadboard, with the behavior there are hard to predict and evaluate in worst-case scenarios. They also express concerns about the new systems type protocol has more bugs and question how we can issue appropriate downgrade if the reinforced learnings approach fed. This lead to us to refer our implementations on reinforced learning schedule. Indeed, it introduced additional ways for our users. After several rounds and discussions with our SRA team, we desire a safe deployment stage as shown in this diagram. We run both reinforced learning disk carrier and loop-based disk carrier concurrently. We add extra components to evaluate and select the best decisions from both. We also set up assistance to reject invalid disk carrier requests. If the reinforced learning disk carrier become unstable or crash, we automatically forbid to the loop-based disk carrier. This approach gives us room to improve our reinforced learning disk carrier while issue the stability of our Kubernetes clusters. After deployment, we collect the data on the disk carrier trajectories for both disk carrier and compare their defense to identify the issues with the new reinforced learnings disk carrier. This helps us improve our reinforced learning occurrences when training the agents for better future performance. Now we have deployed our new new reinforced learning disk carrier to a production cluster with 100 nodes. Moving forward, we plan to expand its coverage. However, there are still runs for the improvements. Currently, the initial hyperparameter scores such during agents training is quite time consuming. We are exploring the use of imitation learnings to reduce this. We also see some research in these areas haven't applied to our trainings. Other areas could be improved is that our framework to continuously utilize production data. We also plan inclusive with SLA and other dimension in the observation and reward space. As our cluster service with different latency SLA sensitives, as we expand to more production clusters, we end to accommodate this need and issue our disk carrier satisfy the scenarios. We also consider replacing the backbone of neural network to attention-based neural network to see whether we can achieve better performance. Okay, that concludes today's presentation. Thank you all for your attending. The day after tomorrow, we will have another session that use machine learnings for time series forecasting to optimize resource utilization and reduce cost. You can check out by scan the QR code here and look it forward to meet everyone there again. Thank you again for your attendance today. Okay.


You can't have too much of a good thing, they say, or maybe it's the opposite. I don't really know. It doesn't matter because the movies I'm talking about today stopped having good things after the first movie or maybe they made it to and before it became a total disaster. Today, I want to talk about the top five dumbest, worst, most awful, a little bit redundant movie franchises still operating today, still thriving in some cases. I don't need to waste any time. Let's begin. If you are subscribed to this channel, you might be scratching your head going, didn't Adam just cover this except for he did 10 of them the other day on a live stream? Well, yeah.   That live stream is still up right now. You can listen to it. Please don't watch it. Please, it's unwatchable really anyways. So you can listen and I think the audio is okay. I haven't I refuse to go back and listen. So what I thought I would do is instead of top 10, I would do five and I would go into a little bit more details about not only where they went wrong, but steps they could take to fix or maybe reboot or whatever. We're not going to walk away. That's not an option. These franchises are too popular. They make too much money. The studios will not walk away. So we are operating under the assumption that these will go on until we all die and they will live far past us. With that in mind, let's go to the number five spot, which is Terminator. James Cameron and Arnold Schwarzenegger made two movies to be very proud of. Terminator 2 especially, still considered one of the greatest action movies of all time. T1, and I don't think anybody calls it that except for me just this one time only. That's considered a really good horror film. And it's right to be considered a really good one because it is. A single machine hell-bent on stalking and killing its prey, sent from the future to fix the past. At least fix it in the game for the machine so that they can sustain their life and go on living prosperously. What I love about these movies is how gritty and grounded they are. But things quickly turn into hell in a handbasket as we get to T3. It's got a supermodel villain. It's not scary or threatening. The characters have all been replaced. Sarah Connor's killed off screen unceremoniously. The Terminator is back in a third version of itself. It's so bad.  And you can like this movie on a superficial comical standpoint, but to put it up on the same pedestal as T1 and 2 is asinine. And I don't think anybody actually does that to be fair. I will also say this. There are Terminator movies that come after 1 and 2 that are at least fun to watch. They're engaging. They're silly. They're dumb. And it's entertaining popcorn flick, humor and action. And that's okay. That's fine. No judgment day if you like those. I don't mind dark fate. I've gotten on record. I know I'm in the very small minority here that likes that film, but I don't love it. And I certainly don't put it on the same level as Terminator 1 and 2.  It's a soft reboot. It does a disservice to original characters, much like Terminator 3 did. Much like Terminator Salvation did with its dumbass plot that makes no sense. Much like Terminator Genesis. There's so many of them and they're all kind of dumb at the end of the day after 1 and 2. And so what I would like to see the studio do, since we can't walk away, of course, Dark Fate was a financial and critical, I don't know if it's a critical bomb. I think it did okay with critics, but it was definitely a financial bomb. It might have been a critical one too. Don't quote me. But what we need to do is walk away from this and we need to go back to the basics. Let's do what Prey did for the Predator franchise. Let's build up a singular character again. And yeah, I guess it would be kind of a soft reboot, wouldn't it? But I don't want it to be called Terminator. I don't want it to have anything to do with the Terminator name. Call it the machines. Call it, you know, whatever. Who gives a crap? As long as it doesn't have Terminator in the title. But people, audiences will know that this is somehow connected to that storyline. Much like Prey did and I think it worked for many. It definitely worked for me. I love that movie. It's its own thing, but it definitely takes a lot of inspiration from the original. But it goes a different route. It goes a different tone with it. That's what I'd like to see. All right, let's talk about number four on the list, which is... Transformers. There are five Michael Bay Transformers movies. There is one prequel movie called Bumblebee that I think later was retconned to not actually be a prequel to the Michael Bay movies, but a prequel to another timeline which features the rise of the beasts or whatever this new-ish Transformers film was that came out last year.  Watchable, yeah. Of course, if you like high octane action explosions, some fun one-liners, little quips, some comedy. Michael Bay is pretty good at all that. Especially in the first one. I really like the first Transformers film. I liked the vantage point of taking it from Sam Witwicky's point of view. Sam's looking up. He sees all this chaos. I don't know what's happening. I don't know what's... I don't even think the animators know what's happening. They're just putting a lot of CG together. But it looks good.  It doesn't look near as good as Transformers 1 does.  Now it's just churn and burn, baby. They don't got time. They got 13 more projects in the pipeline. But Transformers 1 gets it right. It does a good job blending these new characters with the Transformers that we love. And sure, yeah, a lot of them kind of look the same-ish. Not a lot of differentiating between Star Scream and Megatron. A lot of just, you know, kind of sharp edges and metals. But voice acting was on point. I don't know. I got a soft spot for Transformers 1.  Every single one gets worse. And by the time four rolls around, Shia LaBeouf's gone. Megan Fox has been gone since the third one.  Who wanted Mark Wahlberg? Oh, looks like you found the Transformer. Mark Wahlberg. It's lame. It's so lame. Bumblebee was fine. It was a nice little charming quaint picture, taking things back down to basics. But this new one, again, it just goes for broke already. It doesn't kind of ratchet up calmly. It goes way too heavy. The Planet Eating Transformer Unicron is already in the mix. He's the big bad guy. I think they're going for a Thanos thing here where he's the threat, but he doesn't really fight them.  And then they introduce the GI Joes at the end of the picture. What is even going on anymore, Hasbro? What are you doing? What I'd like to see from Transformers is, I think, something they're already doing. There's a new animated Transformers film coming out this year. A lot of big names attached to Voice. I haven't seen a single ounce of animation on this thing, no trailers, no teaser, but I'm guessing. I'm hoping it's going to be done in the same way into The Spider-Verse was done, or the new Ninja Turtles movie, or Puss in Boots 2. I hope it has a cool, stylish look to it. And I really hope they go for broke on what made Transformers so fun. The cool action, the great characters, the fun voices, the cool cars and different vehicles, and just go all in on it. I think it'll be an easy sell for people. And then from there, the sky's the limit. Then we can slowly ratchet things up for the next couple films. As for the live action movies, I just, I don't care at all. I think that they tried something with Bumblebee, but instead of going with that, they led right into the Michael Bay movie at the end. Sam's car, Bumblebee's in the dealership parking lot where Sam's going to buy it and take off.  There's more money to be made. Let's spin off into another thing. Let's get the Beast Wars stuff going on. It's not interesting. And I don't know if they're going to continue with that, I kind of hope they don't, but I think I have a feeling they're going to do the G.I. Joe thing and bring it home. So let's focus on the animated one. That's where my interest lies. There are somehow six mainline Resident Evil films with Mila Jovovich or Husband Directs, I think all of them. These are schlocky at best, horrifically terrible at worst. The first one's fine. I wouldn't even say the first one's top level. Like, it's not this great, amazing movie, much like Fast and the Furious. It does enough well for this base of people that really like that kind of stuff. But for everyone else, I think it was a head scratching film that sometimes work and occasionally goes into a really silly, weird narrative level, which the games all do as well. The story of the games is complete nonsense with the Umbrella Company and Ada Wong is in the mix as a spy and Leon and all these characters come and go and I can't keep heads or tails of it. And I don't think the movies do either. So they just kind of take the things that they really like from the games and smash it together. I gave up on this franchise very early. I think I maybe made it to three. But two was so terribly done, I thought. Just it felt like the budget wasn't there for what they were trying to do. And then they continue to keep pretending they're better than they are by pulling things from other film franchises that are way cooler, like The Matrix. A lot of the later ones do really feel like they're making her Trinity and she's doing this horrible Walmart version of Bullet Time and there's just no budget there for it or they're really utilizing it in the worst way possible.  Now I think the Mila Jovovich films are done. There was a final chapter. But then we are continuing on with the Netflix Resident Evil and there's the Raccoon City movie that came out a year so back that was atrociously bad. They will not let this thing die. Because at the end of the day, zombies don't. How do I fix this? I say with this when you start over and I think they've attempted to several times but they haven't got it right yet and because there's money to be made, let's do it again. Start over. Maybe take it from one of the later games. Go into the redneck one or Resident Evil Village and go with the vampire chicks. The big gal vampire from the village. She was very popular online. A lot of search results for her hit I saw. So let's go with that. That's easy money. Go with the vampire sisters. Carry the werewolf stuff over. Really go into the scary, dark, disturbed creature feature route instead of just generic zombies. Zombies are played out. So let's play with something else. The number two on my list pains me to say I'm wearing the t-shirt. Steven Spielberg, huge super fan here but Jurassic Park really, really has gone through the ringer over the years. Now, I have a soft spot for dinosaurs. I think a lot of people do. They're cool. They're just cool and we haven't seen them in person. You know, we only have the bones. We only have the sticks and the debris and whatever is left of these things. And since for some reason, Jurassic Park has a monopoly on dinosaurs. These are the films they keep getting made. Outside of 65 that came out a year or so back, there's just not a lot of dinosaur material often released. So we get what we get.  The first movie is one of my favorite films of all time. Lost World has its fans. I'm not one of them. Is it watchable to a degree? Yes. But it also features some of the worst things the franchise has to offer such as gymnast girl kicking a raptor out a window like a Charlie Chaplin scene. It's got super unlikable characters with Vince Vaughn and Julianne Moore. But then on the other side, it has a phenomenal RV section where that thing is going out the side of the cliff. It's got the raptors going through the weeds. There's some good stuff here. Unfortunately, it overstays its welcome. It has a very cartoonish final act. And then we go into Jurassic Park 3 that's like 25 minutes long. Shoe string budget. They blew all of it on a different version of a film than ever got to see the light of day. Lots of problems with the animatronics. Lots of problems with the script and the directors. And what we end up getting is honestly, I think, an hour and a half movie. It's just in and out. It's a cup of coffee. It's a watchable digestible trash that I don't need to go back and ever see again. But on occasion, I feel like, you know what? What's a really schlocky movie that's in and out pretty quick? JP3. Let's put it on. Now we get to Jurassic World. It does go the soft reboot route. I think it does more right than it does wrong. Characters aren't near as likable though. Owen by Chris Pratt, who's usually pretty fun, loving, charming. He's just NPC levels of territory here. Just your stock, you know, like holding the hands out. Super strong, cool dude that has nothing to bring to the table. And that's very apparent when the second and third Jurassic World entries kick in and his character kind of changes. Claire's character completely changes. They just have nothing to go on with these people.  And the animals get freed at the end. And there's this whole Kakamemi scheme where the rich people are selling off the dinosaurs much like they did in the Lost World because nothing's original anymore. But they are trained weapons.  Just shoot them! You have the gun! Just shoot them! Listen, I bitched up a storm about these movies. There's plenty of rants and stuff on Jurassic World following franchise and the following one that came after. I don't even remember the name of it. Dominion? Dominican Republic? I don't remember what it's called. It's terrible. It's about Locusts. Giant Locusts destroying the crop field. It's about Owen now able to do the talk to the hand thing to every dinosaur it comes across. It's about baby blue getting captured. So blue's pissed. It's about 14 different story lines that are all uninteresting and really go nowhere new or fresh or fun or engaging. It's not scary.  It kind of turns into a James Bond movie for a while.  I hate it. And now we have a seventh one in the works. It's never going to end because people love dinosaurs and they're willing to sit through the garbage just to see some cool T-Rex action. So we have another Jurassic World movie on the table. Who knows what this one's going to be called? Jurassic Universe? Jurassic Galaxy? We need to go backwards. We need to go backwards to Galaxy. Then go to Universe? I don't know. I don't know. What I would say for this franchise since again we have to keep it going. Get rid of all of these people. Get rid of Owen. Get rid of Sadler. Get rid of all the originals. Malcolm. We keep dragging their corpses out. But it was a one and done with these people. First movie was magic. Lightning in a bottle.  Get them out of here. We start small again. We got to go smaller. Not larger. It never works when you go so big that no one can connect to anything. Make it a horror film. Set it in like a suburban area where it's just a single dinosaur or maybe it's just a small pack of dinosaurs have broken out of this mysterious park that's being worked on or the park has been closed down.  Or I don't know. It's so far gone. I don't actually know how you fix this franchise. They already soft rebooted. So you can't really do that again. And they've already gotten so large that you can't even really pull back. It's just too far gone. So you have to find a way to just get like one or two people or an individual group of people into a scenario where they just have to get out against all odds. Make it a survival horror film. Okay.  You know what? Like it's a revenant type film but with dinosaurs. That would be cool. That would get me back into the theaters. But what am I saying? I'm going to be there. It's dinosaurs. Last and certainly least is the number one franchise on my list that continues to thrive fast and the furious. Everybody knows we are 10 movies in. 10. 11 if you count Hobbes and Shaw. 12 if you consider the fact that they at least have one more planned if not seven. These movies will never die as long as Vin Diesel has a beating heart. These movies are so embarrassing that Vin Diesel is one of the highest paid actors in a Hollywood. This guy has the easiest set of jobs ever. He records I am grouped and that makes him tons of residuals. What a treat. Then he just has to go hang out with his buddies for a few months in some lavish area or stand in front of a green screen or at this point he's probably all AI. The last couple movies I don't think I see any emotion come from this guy. He elicits nothing other than time to ride, family. You messed with the wrong family. Brother? Oh yeah, that's right. I have a brother.  These movies are never about family. Just saying something doesn't make it true. It's the opposite of family.  They're constantly putting each other in harm's way. Going into the backyard occasionally and drinking a corona with some rap music does not make you a good group of individuals to watch. There is just no chemistry or there's just nothing to these. They really are like two and a half hour explosion gasms smashed all over the screen. The plots are impractical. They're insane. They're over the top. And all that stuff can be fine. If you're at least following someone that has presence, that has that star power, and Vin Diesel just has never had that for me.  And I know they said the last one was a little disappointing from a financial standpoint. Well, yeah, when it costs like a billion to make, obviously it's not going to make as much. These things are so overblown with the budgets. Now let's pull back. How would I fix fast and the furious? Well, I personally don't like any of them. Not even the first one is my cup of tea. I'm not really into muscle cars. I've already seen point break. So the plots are already recycled from that. This does nothing unique or different. Paul Walker is the only character that can kind of act in these things. Vin Diesel just never did anything for me. What I would do as someone that doesn't care in the slightest, but thinking about people that actually like these movies originally, is I would get back to street racing. I would just go back to street racing. I don't even know if I'd put Vin Diesel in them. Or what I'm thinking is you find a way to pass the torch. Diesel's no spring chicken, okay? I really don't think at the end of the day, that many car enthusiasts or fans of this franchise really care that much about Dominic Toretto. I think that they just care about the fast cars, the good looking women, and everybody's aging up. So let's get some young blood in here. Let's take it back to the streets again. Maybe I'm wrong. Maybe they just want to keep seeing Vin Diesel jump off CG for the rest of his life. Maybe that's all they want. That's all they ever wanted. I don't know. But I do know this. These five film franchises are completely out of control.   Let me hear from you, though. Do you agree with me? Or do you think, Adam, there's a reason these are number one for a reason. People love this stuff. They can't get enough of it. Let me know in the comments below. Please like the video if you enjoyed this. Got something out of it. Subscribe as I post tons of movie content each and every week. Would love to have you stick around. And if you want to see me in person, I am coming out of the cave for the first time ever. I will be at Orlando, Florida in just a couple days after this goes out under the internets. I'm going to be at Megacon. I'll be there Friday, Saturday, and Sunday doing three different panels with some fun movie critics. It would be awesome if you could attend. There's still tickets available, at least for the day-to-days. I think the super pack or whatever sold out. But there's still dailies you can get. I'd love to talk movies. I'd love to hang out, grab a bite to eat, do whatever. I'm around. I'm ready to go. Alrighty. Hopefully I see you next time. Take care.


Ryan Christie, over the top, lovely ball for Billing! Christie's gone across to take it again, they take it short, the Terries here, two-on-one opportunity, and now Antony back to the top of the box, Jordan Zamura. Then Antony again, Christie desperately hanging on the left side, trying to stay onside, which he's managed to do. Ryan Christie in, it comes towards Salahki with a header! Punches the ball down, goalkeeper's got no chance, right in the corner. Short corner again taken by Luton, taken to Jordan Clarke, to the top of the box and Cornick, who takes a touch outside the D, picked in by Naismith, headed clear by Billing, then Lerma and Salahki will combine, they should have combined to clear, but Salahki miscued his clearance, and now a chance inside, right from Berry, will try and flash it across, goal, and take advantage, Mark Travers is hurt in the build-up, but is it flashed across the six-yard box? It looked like the last touch came off Lloyd Kelly, and Luton have got the goal they've been threatening with 26 minutes remaining. Terries two, looking one.


Okay, for the last couple years I've made over a million dollars a year from a weekly email that I've been sending to my database since 2007. What's up everybody? Ricky Kruth here. Welcome back to my channel. So today I want to talk about my weekly email, which has been the entire foundation of my real estate business. It is the glue that holds everything together. I built my entire brand around this email and for me email is just the way to go. People are checking their email constantly. You probably check your email 50 times a day, all right?  Before we get into all the details and everything, I want to let you know that this Thursday I'm going to do live training right here on this YouTube channel and I'm going to go in depth about the weekly email. I'm going to do a screen share of my computer. I'm going to show you how I create it. I'm going to answer questions. If you have questions, be sure to tune in. Ask the questions and I'm going to go a little bit deeper than what I'm going in this email. Today I want to talk about more of the philosophies behind it, why I do it, and stuff of that nature, and this Thursday I'm going to go in depth about how I do it. And by the way, this is the first time that you're seeing me. I want you to know upfront that everything I do here is 100% free. I'm not trying to sell you anything. I just want to share everything and what I'm doing in my business that has made me successful and that way you can take whatever you want out of it and go succeed yourself. So with that, I have a couple more tools for you here and the link in the description will take you to Constant Contact where you can get my new e-book which is called How I Make a Million Dollars a year using email. You also can start a free trial with Constant Contact which is the company that I use to do my weekly emails and they will give you the exact email template that I use on my weekly emails. I will also link my last week's email in the description below so you can see an example of what I do. So I'm just trying to give you all the tools that you need to make this happen. And my rebuttal of that is if you're not doing it every week, if you're doing it every two weeks, if you're doing it once a month, then people start to think you're a slacker. Okay, it's not frequent enough. People get hundreds of emails every single day. Okay, if they can't handle one a week from you, then what are we doing here? Are people going to unsubscribe? Yes. People are going to unsubscribe from your monthly email, from your bimonthly email. You're not going to prevent people from unsubscribing. Okay, so trust me when I say I've been doing this since 2007. I've made over a million a year for the last two years from this exact activity and I'm here to tell you that this is the exact frequency that you need. Okay, don't alter what I'm doing here. Do it every single week on the same day of the week forever and they're going to love you. So the subject needs to be the same every week. It needs to be whatever you want it to be and the date. You know, I'm in real estate, so I do Gulf Coast Market Report and then the date. It's been that way for 12 years now and the subject brands itself. When you change the subject up, people kind of get, again, it's kind of lost in the mix. Okay, they don't recognize the subject and they're just going through all the other 100 spam emails they're getting and yours could get grouped into that if they don't recognize that email subject line. So the subject line is really important. I think it should be the same every week so that you're branding not only yourself and the information you're providing but also the subject line so they know what that is every time they see in their inbox. Another thing is original content every week. You need to create this yourself every week. Do not do automatic drip campaigns. Do not do the automated emails. People want original content and not only that, they want original content from you, the expert. They're professional that wants to help them through and consult them through whatever it is that they're trying to do. They want to hear from you every week and know what's on your mind, what you think, what's going on in the market, so on and so forth. So it needs to be original content and this is where you're going to win. If you do automatic emails, there could be a good chance that you're not going to catch the same traction at all. Matter of fact, I know you're not going to catch the same traction at all as far as building your brand, especially if there's a couple other sales people in the same area sending the same exact automatic messages. It doesn't look good. It needs to be original content and I spend the time to create that every week. It takes me about 30 minutes to an hour to create this email and send it out and this is a way I can scale my business. It doesn't matter if there's 5,000 people or 5 million people on that list. It still takes me the same amount of time to create and send that and make that impression. Speaking of impressions, I did an email a couple weeks ago called email open rates don't matter and I went in depth about why the open rates don't matter. Everybody's concerned with open rates. More interesting to me are the people that didn't open it but saw that in their inbox every week and knows how consistent it is and is really on your team and really wants to do business with you. They're just not ready yet. I would really highly suggest if you haven't seen that video, go back and watch that video as well. There's a lot of good information there. When you do this email every week, it shows how consistent you are. It shows how dependable you are. It shows how professional you are. It shows how knowledgeable you are and through the original content it shows how personable you are.  Even though they know it's a bulk email, they still fill a little bit of that personality within the email and that's what helps you win them over short and long term. The weekly email to me is basically a net on the backside of my business. When new clients come in, whatever happens there, we do a deal, we don't do a deal, either way it goes, that net catches them forever. They now get that weekly email and I start to brand myself. A lot of the business I do, most of the business I do, really a good percentage of it is from people that have been getting this weekly email and five years ago they met me. I cold called them, maybe I showed them some property, whatever the case may be. They started getting this email that didn't do a deal at the time. Five years later they're ready to do something. Who are they going to call? When you do this with every person you meet and this email database just starts to grow and grow and grow and grow, it's a snowball and after a while you start to reap the benefits of all the people in your past who now are ready to do stuff and it just grows and grows and grows. It's an incredible way to build your brand, to build your business and to really put your business on autopilot because when you build it up to the extent that I've built it to, I don't have to make hardly any calls anymore because I have so much business coming in through repeat business referrals and referrals of referrals. This is how you capitalize on the relationships that you build and those great conversations that you have on a daily basis, even if they don't want to do a deal today or not. This is how you capitalize and if you have a great conversation with somebody, they like you enough to do a deal, they're just not ready yet. That's where all the success is in any business really and if you can capture those people with that weekly email after you've made this incredible impression with them face to face or voice to voice, now that person is worth 10 to 20 deals to you over the life of your career in this snowball effect business that I want you to create. I want you to create the same type of business that I've created where you've made so many calls that now people cold call you and say I'm ready to do this, I'm ready to do that, I'm ready to buy, I'm ready to sell whatever the case may be and you are their real estate agent. So I'm really looking forward to this live training Thursday. Be sure to subscribe here and also go to zero to diamond.com and sign up for the absolutely free real estate coaching program. Be sure to comment below if you have any questions that you want me to address during the live session. That'll make it easier for me to know those questions up front going in rather than all the comments because there's going to be thousands of comments and sometimes it's really hard to decipher through all those comments. If you comment on this video I'll know going in and I'll be prepared to answer that question. So thanks for watching. Let me know if there's anything in the world I could do for you and we'll talk to you soon.