Thank you very much. Thanks for the invitation to come and speak. In the spirit that this is some of these opening talks, a bit of a review, I thought I'd go back and start with one of the first papers that I know of anyway, which kind of discusses the Kedl climate variability a little bit. This is Kinzer from 1933, which has a whole series of long temperature records stretching from the 1890s up to 1933. It clearly shows there was a trend at the time, but also lots of variability as well. Particularly in Greenwich, for example, you can see the Kedl variability. This also highlights that the tropical regions on the right-hand side tend to have less noise, less variability, so even at the time they knew that the tropics were less variable than the extra tropics, for example. We've also been simulating climate variability for a long time as well. This is from the first IPCC assessment report in 1990. It's a personal communication. You probably wouldn't get away with that anymore. This is one of the first GCMs, control simulation of one of the first GCMs in 1990, and you can very clearly see the Kedl variability in global mean temperature in this very first, one of the first GCM simulations. Now, what would be really nice if we could go back and rerun this with rage-forcing turned on rather than the control simulation, so we can do that. This is what would happen, we think. This is trying to make the point that you can see pauses, surges, all kinds of variability superimposed on top of a long-term trend. Back then, we knew that we should expect to see periods of rapid warming and periods of slower warming. How does all this relate to local scales? No-one experiences global temperature directly. We experience it, temperature changes locally. This is one example just showing global temperatures in red and central England temperature, where we have very good records stretching back a long way in black. Obviously central England has a lot more noise. We have very variable weather in the UK, but you can still see the fingerprint of global temperatures on the UK temperatures. You can see the familiar warming during the early 20th century. You can see the cooling or flat period during the 50s and 60s and 70s and then a rapid warming at the end. You can see that fingerprint of global temperatures on a local scale. You can see by the correlations listed on the top here, as soon as you start smoothing the data a little bit, the correlations are extremely high. That's the temperature. That's not true for rainfall, for example. If it's, say, summer rainfall, which is something we might want to predict, is far less correlated with global temperatures than temperature, as you might expect. The correlations here are basically zero. You might say there's something on multi-decadal timescales, but it's much weaker than for temperature. Global temperatures doesn't tell us everything. There's a lot of variability on regional scales. Just continue the history a little bit. One of the first studies, I think, that I found anyway, which discusses how variability might affect future trends. For example, this is from a Clifar document. Clifar has been thinking about this for a very long time. Clifar exchanges in 2001, Sutton and colleagues. The observations have been white at the time, and this is a small ensemble, but at the time it was a large ensemble of three simulations for the next 20 years or so, showing that there was a long-term trend but interacted with variability as well to show that you would get very different trajectories possible in the future. This is what motivated, I think, a lot of the decadal prediction was this type of activity back in the early 2000s. Of course, now we can run much larger ensembles. This is one example from Clara Deser's work with the CCSM large ensemble over Europe again. Most of us are probably quite familiar with this work, but they run a very large 40-member ensemble forward from a modern time. For example, this is European summer temperatures. The top left shows the average trend over 55 years in this large 40-member ensemble. That's the mean. But if you look at the individual simulations, you can see a warmest and a coolest simulation showing a very, very different temperature pattern of trend. This is a 55-year trend. You can see very, very large differences in what might happen. These ensemble members were all started from the same initial condition, just with small perturbations to the atmosphere. This essentially is the butterfly effect, the chaos, causing these divergences in the simulations. That's quite a large change just from that small perturbation in the initial conditions. Just to highlight that a little bit more, there are the time series from certain points in this ensemble. The red line shows the warmest member and the blue line shows the coolest member. You can see there's quite a dramatic difference between the different ensemble members just with a small perturbation to initial conditions. For example, Paris or France or Oslo, the example shown here, you can see very, very different 55-year trends. Whereas for the global temperature trend on the bottom, they're much closer together, but not identical. This raises a lot of questions. Is this true in other models? This is one model they did this in. Is this model realistic? Will this happen in other models? What happens if you perturb the ocean in initial conditions as well as the atmosphere? We decided to test some of this in quite an idealised situation. We used the famous global climate model, which is a very coarse model, which means it's very fast, which means we can run lots and lots of ensemble members very quickly. We did a more idealised situation. We did a 1% per year increase in carbon dioxide for 140 years. We have a long control run, and we picked at random one particular initial condition from that control run and ran 100 members initialised again with a very tiny perturbation to identical initial conditions. You can see the ensemble here for the European winter temperatures as one example on the top right. Again, you can see the differences in trends are extraordinary. There's one simulation there which shows a very rapid warming, another one shows a very rapid cooling over 30 years. The bottom right shows a histogram of trends, of 30-year trends. Again, you can see members with a strong cooling and members with a strong warming, but there's a very, very wide range. To explore what the ocean does, we also ran what we call a macro ensemble where we took 30 different members from 30 different coupled initial conditions well separated in the control run to try and understand what effect that would have. You might imagine, of course, that if you build in more uncertainty initial conditions, then you're going to amplify the spread even more, and that's what we see. Again, we have the same two diagrams as I just had before for European winter 30-year trends. The top right shows the time series. The middle right shows the micro histogram, and the bottom shows the macro histogram where you're just taking into account the ocean initial conditions. The spread is a lot bigger, but also the mean is very different. In the macro case, you're seeing a lot more warmer members than you are in the micro case. So it turns out that in our random selection of our micro case we happen to pick a particularly unusual situation, and I'll come onto that in a moment. If we now look at, again, these average in the same way Clara showed in her work, the average trend, so you can see the left column shows the micro ensemble and the middle column shows the macro ensemble. In the micro ensemble, you actually see an average cooling over some parts of Western Europe, whereas you don't see that in the macro case, which samples the ocean initial conditions. If you look at the maximum and minimum ensemble members, again, you can see almost any trend you like from plus three degrees to minus three degrees over 50 years. That's the individual members. You can also look at what happens if you select your grid point independently. Again, that saturates the colour scale and you can get any result you like just from perturbing the initial conditions very slightly. What happens with rainfall, you can also do this with rainfall and again, you can see a very strong wetting or a very strong drying depending on the ensemble member you look at. What's going on in this particular ensemble? We now look at the time series of European winter temperatures and you compare the two ensembles. The micro ensemble is doing something a little bit strange whereas the macro ensemble is, as you might expect, just a more linear warming because it's a straight 1% per year increase in CO2. What's causing this effect in micro is obviously some predictability and some memory here in the ocean initial conditions. It turns out that in this particular ensemble member the ocean state that we picked, Amock was in a particularly predictable state, for example, so all of the ensemble members had a very strong strengthening of the Amock followed by quite a strong weakening of the Amock and so you can see that reflected in the European temperatures. There's also some predictability here but it's obviously going to be important to sample the ocean initial conditions when you're designing these ensembles as well as just perturbing the atmosphere. Just one final example of what effect that has if we look at the probability of a cooling trend over the first 20 years of the simulations when we're counting for the ocean initial condition uncertainty we see quite a sort of a broad blue colour, light blue colour over much of the world extra topics, whereas in the micro case which is this very specific ocean initial condition we see a very strong probability of a cooling over certain regions in the Southern Ocean and the North Atlantic. So again this highlights the differences between these ensembles and the need to sample different ocean states and as you go further ahead in time you can see they do gradually get closer together and there's less chance of seeing a cooling trend but it takes 50 years for these patterns to converge. One little example if you could also in this very large ensemble we have lots and lots of members we can find lots of members which have zero trend over say a 15 year period even when the CO2 is increasing so all of these four different members have exactly zero trend over 15 years and these are the trends spatially over those 15 years and you can see that the patterns are you can see actually the first two there are almost exactly opposite so there's all kinds of different types of variability trends which can offset the warming in this particular model. I should say this particular model does have a slightly enthusiastic variability the variability is probably slightly too high compared to the real world but the principle is there and you see lots of different types of height as possible in this model and I think that would be interesting to explore a little bit more. How does global temperature go in this particular model so this is the histogram of all the possible trends in global temperature over a 15 year periods and 1.2% of them have a cooling trend for example if you then subsample the 15 year trends following those 1% that are cool so we take the 1% of members and look at the trend for 15 years and look what happens in the following 15 years we see the distribution has shifted slightly to the right so you might expect this to happen this is kind of regression to the mean if you like after there's been a pause you actually see more higher chance of a surge in the following 15 years that's maybe not a surprise and I'm going to finally finish with the point that Gavin brought up this study a little bit earlier on a different topic slightly it comes down to how we compare our models and our observations so the top panel shows quite a familiar type of diagram comparing the observations of global mean temperature with the CMIT 5 ensemble and say that the recent period you see that the observations at the lower end of the ensemble range the red line shows the normal projections from the CMIT 5 ensemble and the top panel just looking at the top panel to start with but of course when we're comparing the observations we're not comparing apples with apples because in the observations we are using air temperatures over land but we're using SSTs a sea surface temperatures over the ocean and it turns out that the SSTs warm slightly slower than the air temperatures we're in the models we're just sampling air temperature everywhere and so this small effect actually makes quite a it makes a difference to this comparison so that's the difference between the red line and the blue line so then if we treat the models as observations and subsample from the simulations where we have observations and the same and we account for the observation type so we blend the SSTs simulated sea surface temperatures with the simulated air temperatures over land it makes a difference and it nudges down the models from the red line to the blue line if you then account for the updated forcings that Gavin described earlier as well the same comparison in the bottom panel so as Gavin said if you update the forcings it nudges down the models towards the observations and now if you look at the blue line in the bottom panel which accounts for the updated forcings that Gavin talked about and this using blended temperatures with the simulated temperatures over the ocean and the S80s of the land you see that the blue line is now much, much closer to the observations and so it turns out this how we do this comparison is actually extremely subtle at some of the details at how we do this so we have to account for the variability we have to account for the forcings we have to account for the fact that we don't observe everywhere and we're not necessarily comparing like with like all the time so in summary the climate and our simulated climate certainly exhibits substantial natural variability in temperature, rainfall also sea ice I haven't talked about that but that's also true our models show a very large diversity in these characteristics and we've talked about that briefly earlier as well and we need to better understand those differences if we're to robustly attribute what's going on in the observations these large ensembles which more and more groups are running now are very valuable tools to explore possible outcomes of regional scales we need to think about how we communicate and visualize this variability as well we need to communicate to the fact that to the public that we don't necessarily expect temperatures to go up all the time and we do need to be very careful when we perform these comparisons on models and observations that we're comparing like with like otherwise you might come to the wrong conclusion there's also a poster discussing in this similar topic discussing the sensitivity to reference periods which is also again quite subtle but also quite interesting thank you


It's LinkedIn Learning author Monica Wahee with today's data science makeover. Watch while Monica Wahee demonstrates how to use the package ggplot2 to make a pie chart in R. Hi everyone! Today we are going to use the ggplot2 package to make a pie chart. You will see this code here which is actually a long file of code that you can get off of GitHub. The pie chart code is the first code on there. Actually I took a video of myself running this code just to have a video to put in the background of blog posts and things where I need a video running. I actually feel kind of special about this particular pie chart I demonstrated because the data are from a publication I made with one of my favorite colleagues. You can go to the blog post I linked you to and read about that. You might think a pie chart is pretty simple you know compared to time series charts and more complicated plots but actually pie charts are not that straightforward in ggplot2. I'll show you. Let's start by running this code up here. This will read in our data set which I called pie chart underscore df and also call up the ggplot2 library. Okay let's start by looking at our data frame. I'll run our data frame which as I said is called pie chart underscore df. So here's the data frame. It has two columns. In the first column it has a proportion and then the second column is called type and has different types of media assignments you can have in college. Like for blog you could be assigned to write a blog post or like the next one digital storytelling you could be assigned to tell a digital story and so on. So we had blog, digital storytelling, make an e-portfolio, make a podcast and by far the most popular one make a video. My colleague and I were reviewing studies of deeper learning approaches in higher education and this was the distribution of studies we found where they assigned media to be made. So this distribution is what we are making a pie chart of. Let's go back to our code. So actually before we go too far let me point out this color vector. See this vector? It's a character vector called pie underscore colors. It has a list of mapped our colors in it. Actually let me point out that it has exactly five colors. One for each type of media we just looked at. We are going to use this vector in our ggplot2 programming to color the pieces of our pie. Let me just run this and create the vector. Okay now that is in memory. Let's tackle this ggplot code. So let's cut to the chase. The main weird thing about this code is that we don't call any shape called geome underscore pie or geome underscore circle and the weirdness starts on the first line. Look at our aes command. There's no x in it. I just put two quotes for the x. We are not choosing one. y is our proportion column and we set fill to type for the media type. But then look at the next two lines. See that? We are calling geome underscore bar the way you would do in a bar plot. I set the width and the size to one, the color to white, and the stat to identity. Then, and this is really important because this is what makes it a pie. On the next line I have chord underscore polar with the argument of just y in quotes. That is what makes it a pie. Here let me show you. I'll run the code just through geome underscore bar and show you what it does. See that?  But then let's see what happens when I rerun the code this time including the next line with the chord underscore polar y command in it. Okay, now we finally have our circle. But there's still cleanup to do. We have to apply our custom colors as you'll see and we need to do something about those labels and I'd prefer a less cluttered theme. Okay, let's go back to our code. Let's look at the rest of this plot code. The next line is a geome underscore text line. This uses a paste command to format the proportions as rounded percentages with a percent sign after them and put them as data labels. Note the position command. I said position equals position underscore stack and then in parentheses I say vjust equals 0.5. This is a way of adjusting the data label so it has shifted just a little and doesn't overlap things. Next we have the lab command. Notice all those nulls. That's how I turned off the labels I didn't want that we just saw a moment ago. And that's how I made the title say what I wanted. The guides command formats the legend. For whatever reason I wanted the legend to be presented in the reverse order. So that's what that reverse equals true is. The next line scale underscore fill underscore manual replaces those awful default colors with our color vector called pi underscore colors. Next theme underscore classic applies a minimalist theme to suppress all that clutter like that gray background you saw. And then we use a theme command to further suppress some items from the display and to color the title. Okay let's run this new improved code. I promise the pi will look a lot more appetizing this time. Now that's classy. That's one attractive pie chart. Thank you for watching this data science makeover with LinkedIn Learning author Monica Wahee. Remember to check out Monica's data science courses on LinkedIn Learning. Click on the link in the description. Thank you for watching. If you like the video then please hit the like button. Also I invite you to look around my channel and if you like what you see subscribe. I hope this video finds you well and you are having a super day.


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Oh dude forget about it Those that's the that's the nightmare times right there. That's true. That's the nightmare times All right, we do got points cards to random Yeah, that didn't that didn't last long at all I'll never get back up to that, right? I only got my max bench up like 205 Then my shoulders I worked out a good bit. I was pretty fit too. I would walk like About 10 miles a day working at Universal It was a lot a lot of activity and I'd play like Football and not like football like basketball and stuff with friends like a lot like I was super active. It was crazy Yeah, like the opposite of what I am now Yeah That's that I was at five. Yeah five points cards random to everybody You got seven five after seven one Two three four five six and seven I probably should have just done the names of the divisions AFC east NFC north NFC east NFC west In afc south a death grip. Yeah, I should probably I should I might no not really Honestly, no, I could probably do 20 push-ups and like be sore for like three days Like legitimately Dude squad would be so much worse than bench to It would be it would be really bad if I tried to like squatted these an amount of weight Um, I would definitely tear something. All right. Here we go. And enough about that Autos pierce strong jr. Christian watson arnold of aketia Damian pierce calvin also the third sky more george pickens david ojabo romeo dobs dany gray green pulsar and a wandale robinson a conco breeze hull green pulsar tyquan thorton redemption james cook afc east rookie deer contenders auto orange scottar thompson marshal folk to 15 Tyler lenderbaum Bailey zappy lenderbaum there colt turner tyler smith christian watson kairan williams drake london luicine sam howell greg dulcich eric izucanma sauce gardener wandale robinson gold kobe bryant That's really good to lose this All-time contenders auto gerry rice numbered to 35. That's sick on card Legit legit niff kenny pickett AFC north redemption kenny pickett ricky ticket rps variation correct ice kenny pickett One of 22 and nebula one of two rookie of the year contenders auto desmond ritter Great break makes again everybody we're on to the last break of the evening


In this video, we will focus on multiplication of three-digit numbers like this using Urdhvathiriyaq method. In this method where we have to multiply two three-digit numbers, first we perform the vertical multiplication or Urdhva at the right-most digits. Then we perform the cross multiplication as shown here on the right-hand side. Then we perform Urdhvathiriyaq or the mix of cross multiplication like this and vertical multiplication. Then we perform the Thiriyaq or the cross multiplication on the left-hand side of the digits like this. And at the last, we perform the vertical multiplication again on the left-most digits. Let us see how we can do this. So, let us first take the multiplication of one and two and we will write the result on the right and put a slash. So, what we did is two times one which is Urdhva. Then we go for the cross multiplication like this and we multiply two and one, two and one. That gives us two plus two and now we perform Urdhvathiriyaq. In Urdhvathiriyaq, we will multiply two with one, two with one and this two with the one in the middle. The result is going to be the addition of all these multiplications. So, two times one, two plus two plus two. This is how we will write. Let us put another slash. Then we go for the cross multiplication on the four digits on the left like this. So, two times one plus two times one which is two plus two. And after completing this, we go for the Urdhva or vertical multiplication for the left-most digits here which is two times one or two. Now, let us add all these results keeping these slashes basically two and four, then six, then four again and two. And the result of this multiplication is going to be 24,642 by just writing these digits without the slashes. And this is how we can complete the multiplication of three digit numbers. But what if there was a carry generated? Let's see an example. In this example, we will see what happens when the carry is generated in the results. Let's do Urdhvathiriyaq method. So, first we will multiply two with four and we will write eight at the right-most place with a slash. Then we go for the cross multiplication like this and we multiply six times four and three times two. Six times four is 24 while three times two is six and we will add these together which is 30. So, we will write zero and write a small three like this. Then we go for the next step. Next step is where we do Urdhvathiriyaq. And Urdhvathiriyaq is here like this. So, we multiply the one and two, four and three and three and six. We will add all these multiplications. So, basically what we have three times four which is 12, then one times two which is two and then six times three which is 18. And the result we get after adding all these is 32. We will write two and a small three like this which will be the carry for the next result. After completing the Urdhvathiriyaq or the mix of vertical and cross multiplications, we go for the cross multiplication of the digits on the left-hand side. So, three times three which is nine and then one times six is six. This gives us the addition as 15. So, we write five and a small one and then we multiply using Urdhva. So, basically we do the vertical multiplication on the left-hand side which is going to be three. And our result is ready, almost ready. We just need to take care of the carries. Now, focus on these carries. It's a very important thing. This is carry, this is a carry and this is a carry. We want to make sure that we pass these carries to the result on the left. But we will always start by looking at the carry which is at the right most place and we will start pushing this carry to the left. We will push this carry three to 32. We will then look at this result and then push the carry towards left again and then we will look at this result and push the carry to the left. What happens if I write zero here and push the carry to 32? We get 35. We'll write five and a small three. There is a chance that after adding this carry, 32 might change into something like 42. So then in that case, the carry will change. That's exactly why we want to make sure that we push carries from right to left. Okay. So, now this is 35 and then we can push this three. So, I can push this three to the left and it becomes 18 instead of 15. So, I'll write eight and a small one, right? And I erase this carry here. I have a three here. So, if I push this one to the left, I get four and I can erase this carry and I can just write four here. It looks a little messy, but I can still see all the digits as a result and the result is 48508 and this is the result of the multiplication of the numbers, 362 and 134.


What does it take to keep Europe's biggest banks safe? A great team and, of course, a wise leader. For the past five years, Andrea Ria was the head of European banking supervision and is now handing over the baton to Claudia Buch at the end of the year. You're listening to the ECB podcast, bringing you insights into the world of economics and central banking. My name is Stefania Secola.  Expectations were high when the ECB took over supervision of the biggest banks in Europe almost at ten years ago. And a lot has been achieved. We have a strong banking sector in Europe today. It was not always easy and straightforward. But faced with severe tests, our supervisors have exceeded expectations. Our President Christine Lagarde sat down with supervisory board chair Andrea Ria at the recent ECB Forum on Banking Supervision. And they looked back at his eventful term. She asks him about his best and worst memories. He also reflects on the most difficult decision he had to make, his legacy, and his wishes for the future of European banking supervision. Let's listen to their conversation. Good afternoon to you, Andrea. I think a test of your fame, popularity and success is actually the room. Not everybody can see it because a lot of people are watching from online. And the room is full, which is quite staggering after a day and a half of all this hard work and great debate. Because you came, of course. No, Robby. No, no, no, no, no, no. So you've been showered with compliments like hardly anybody I have seen, even upon leaving. And I'm going to ask you some really quick questions, and you're going to have to respond quickly as well. So what is your best memory? I would say the start in town hall. I mean, meeting with the staff, listening to them, understanding what was that was working well and what maybe needed to be changed. Okay, what's your worst memory? Worst memory. Well, I mean, we all have bad memories of the pandemic, of course. But I would say, honestly, that the most tense period has been the spring this year. I mean, now everybody has been saying, well, the European banks have done, how good we have been. Let's be honest, I mean, we have been very scared about what was happening in the spring. And when you see deposits flowing out of the banks, I mean, as a supervisor, you start really freaking out. And so that was tough. What was your most difficult decision? Well, in these five years, of course, the decision on dividends has been very difficult and painful. Because, of course, what I would have liked was that the banks themselves had decided to show some restraint and refrain from paying, distributing dividends and doing buybacks in a moment in which there was such a radical uncertainty on what was coming.  We are seeing projections of GDP, negative growth, 14%, 15%. So, I mean, and still, you know, while moral creation seems to work in a national environment, you know, European Union is still a rules-based type of community. So if you don't come with a hammer of a recommendation, things don't happen. So that was a difficult decision. But I think that although some people also in this room I know disagrees, I still think it was a good decision. So did you have to come down with a hammer? We did, yes. We had to come with the recommendation. How did you use the hammer? Well, I mean, it was simply the basically a recommendation. I mean, agreeing on a recommendation, deciding, issuing, and this delivery.  I don't like to use this hard power. I would have liked much more the soft power of, you know, banks seeing the value in doing that. But eventually I remember that there was a respective observer of, you know, banking issues that tweeted, you know, ECB recommendation not to distribute. All banks say, yes, sir. EOPA says insurance companies, you should not pay dividends. And also from some national authorities, insurance pay dividends in several countries, not all countries, but in some countries. So that conveyed in my view, and it's not a criticism of EOPA, of course, that did a very good job in pushing that.  So the banks did not disappoint you at that point? No, no. Well, disappointed me because they didn't follow moral situation. I would have liked that. But anyway, eventually, of course, when we came with the recommendation, they did what they were asked to do. Those who know you well would argue that at that point in time it was a test of your decisiveness and you lived up to the test. Would you agree? Yes, I must say, I remember very well when I started, let's say, the last phase in 2011 of this working supervision, you know, when I became chair of the EBA, I remember the interview with Michel Barnier, you know, when he was selecting me. First of all, it was one of the most difficult interviews ever because even before, I was told that Michel Barnier didn't want to do the interview in English. So I said, can you do the interview in French? He said, oh, my God, I cannot. That was a bit of a... But eventually, he went and managed. But when he came out, he said, you know, you will have to be very strong in this. You will have to show decision.  And it was tested very, very, very early on. And I learned it's difficult, but as was saying in this job, you need it. And so I'm glad that you say it. I think that, you know, I don't shy away when you need to make a decision. No, absolutely. When that was on your mind and you decided that it was the right way to go, given that moral suasion was not working. What was it like? Well, all right, all the members are shaking here. No, no, the boardroom, we have to remember ourselves, was my, you know, my room at home because we're all working remotely. No fear, yeah. So, no, but I would, let's say, I think the process was, first, let's say, all the discussion individually with the bankers and then with the European Bank in Federation that organized the meeting with the banks that didn't go as I expected. But then I discussed bilaterally with most members and then we had a meeting. And eventually, I mean, there were different views around the table, but I think, and I was discussing with the board this in our last meeting, let's say, we have been very good at coming at consensus. Also, when we don't have agreement, when there has been such a good quality of discussion amongst us that eventually everybody can leave with something that is seen as having strength. So the decision was not difficult. I mean, I had some difficult discussion with some individual members, some of them will remember it, but eventually the decision was not difficult. So what tools did you have that you wish you would have used? Not necessarily in this particular instance, but in general. You have a toolbox, you have used many of them, other tools that you wish you had used a bit more and other tools that you wish you had had, but you didn't have. I'm thinking about Claudia who is going to take over from you, so she also will appreciate the advice. I mean, the remark I was making before on moral creation is an important one.  So we do have it with individual banks. So our supervisory teams, when they engage with individual banks, they have leverage and they can convince the bank to do things and sometimes the bank is not convinced, but still sees the benefit in doing it nonetheless. But when you talk to the industry, to the sector, sometimes it's difficult because I understand that that's the point in which you are a banking union at least on the supervisory side, but you don't have the government side also in the room to some extent. And that can be an element of, and the type of environment at the national level means that people feel more obliged to follow what the supervisor says because it's a community. Here banks bring us to the European Court of Justice without a blink of an eye. In national situations, this doesn't happen very frequently. The bank feels a bit reluctant not to challenge in court the supervisor and that's the difference and we have to learn to live with that. I mean, that's the environment. So what's your track record before the European Court of Justice? Well, Frank maybe would be a better place to reply. We lost several, several judgments. And I think that, and Frank says this and I'm totally 100% with him, bring them on. I mean, eventually if the banks are right, if the court says that they are right, okay, we'll learn and we'll adapt the way in which we behave. But I don't think that we should shy away from taking difficult decisions because we are afraid of losing in court. So seeing you in court is fine? Seeing you in court is fine. Okay. So, okay, well, it's true to character, that's good. So what tools do you wish you really had had and you don't have in the toolbox? To be honest with you, it's not that... And everybody else, huh? I mean, it's not that we have tools. I don't think we are lacking tools. I think the tools that we have in our finding regulations are the right tools. The key problem I think, and this is what is making me a bit upset, honestly, on the discussion for instance for now on crisis management, is that sometimes we have the tools but because of the concerns at the political level from the legislators when the regulations are made, we try to put all the possible constraints to the authorities to not enable them to use their tools with sufficient amount of discretion. So for instance, when we take deposit guarantees, I'm not talking about this, not talking about this, we have done enough of that. But let's say with the current amount of funds that we have in the European Union is in the same ballpark of what the FDIC has in the US. Same ball. But it is scattered all over. It's scattered all over, but single-regional funds plus national deposit guarantee schemes, same amount of funds. The problem is that because of different willingness to preserve national specificities in the way which you use these deposit guarantee schemes or fear of activating too much a single-resolution fund which is the only mutualized part of our safety net, we put so many constraints that whenever we have a crisis coming, we cannot touch basically a single euro of this funding and we have also, so this means that we have very short time frames to try to find a solution to this crisis. And honestly, that's something which is difficult and we have an excellent collaboration with the SRB. We prepare together, we work together and so far we have delivered. But again, I mean, sometimes these concerns that everybody wants to preserve the national bells and whistles makes our life more difficult. We have to activate 21 different legal system when a bank goes into a crisis. And honestly, I mean, we had the crisis of Berba, playing vanilla, a bank that went bust just because it was Russian after the invasion of Ukraine. But still, we had to find a way in Slovenia where the bank was the fourth bank in the country, a way in Croatia, different way. The bank was the third bank in the country, different in Austria. I mean, it's not making our life easy. So having a little bit more, these tools that we have, making them more available, easier to use and trust the authorities. We set up these European authorities, the SRB, the ECB, trust the authorities instead of tying our ends in the legislation. Okay, so that's one of your wishes for the future. Yes. That that can be fixed and addressed. What would you like your legacy to be? You know, I think that there are several things that I'm attached to, but the most important one in my view is breaking the silo culture inside the ECB and between the ECB and the national authorities. And I think we have a fantastic firepower in terms of technical skills, in terms of people who really knows the job, is passionate. So enabling the pooling of these resources in our, in our, in the SSM, across the whole collective house we have and in give us much more effectiveness, much more impact, you know? So we have started doing that. We are not yet finished with the job. Internally, we did the organization. We have an integration agenda with the national authorities, but still a long way to go and we can be even better. But I think I'm proud I initiated this process. But I can say that's outside the interview because I'm not asking you to opine on that. It is that the cooperation between the SSM part of the house and the central banking part of the house, not just through shared services, but in general, in terms of substance, intellect, sharing of data has been fantastic. And I would like to thank you for that. You have to let me speak a bit about that because you are deviating from the script that I thought you were following, so. I don't, for you all to know, he doesn't have my script because I just made it up. No, so basically, the first point I wanted to make, actually, was stressing the point you made yesterday, which for me is very important, because, let's say again, sometimes I'm old, so I've always been a strong believer in having supervision within the central bank. And actually, I've seen in these years the value of having that. I mean, and again, if you had asked me, as I was expecting you to ask me, that how we managed these series of shocks, I think that what was fantastic was exactly to tear down these firewalls between the two sides of the central bank to mobilize the extraordinary intelligence that we had in understanding the economic environment that was changing, getting the statistics we needed on the sectors hit by COVID or by the invasion of Ukraine, pulling together people, junior people also across the house and making them work together and preparing, you know, spot on analysis, real time people working the nights, you remember that, also during the spring. That has been fantastic. And I think that honestly, and this goes to your merit, Christine, I mean, could never happen if they didn't see this type of, you know, contact relationship between the two of us and if it was not for your leadership that I owe a lot to. Yeah, too much, too kind. No, no. So, looking back at all these years and not just the last five, but looking back, if you were to advise young graduates in, you know, let's say sciences, anthropology or economics, what advice would you give? I do that quite a lot when I go, I have meetings, you know, with young graduates in finance or with the student university when I go in my country visits sometimes. And I mean, the simple thing I tell them is, well, consider becoming a supervisor because all these skills that you mentioned can be deployed very effectively in supervision, including anthropologists. And you know, it's a job.  And if you do a single tiny mistake, you are slaughtered in the streets. So, people should really run away from a job like that, right? But instead, all the people that start entering this profession eventually become so passionate about it. I mean, they, and that's fantastic. And you can deploy all these tools and come to know real life challenges that you can see in few other professions. So, I really try to, you know, use these arguments to get a little bit more incoming stuff in our collective, you know, organization. You know, there was a few Netflix series series that were instrumental to encourage young people to move in certain professions, you know. Formula One is an example, but there were a few others. Do you think we should encourage Netflix to look at supervision? I mean, why not? I mean, usually, you know... Would you accept to star? No, no, that's not something I would do well, honestly, I know my limits. That's true to character as well. No, you don't. So, the time remaining for us, my dear friend, is an hour and 15 minutes. You okay with that? No. So, I think we have to come to a close because lunch is next. I know you're not going to take up yoga, but what thing will you do wherever you go next, which you haven't had a chance to do so far? And I'm not asking you whether you have any regret. That was on my little list of questions that I put for myself this morning because you clearly have no regret.  You have all these very inspiring stories of people that, you know, after 60 has changed totally their life and do something totally different. Well, I'm sorry to disappoint. I'm a boring person. And I will, I mean, my main ambition is to continue doing the same boring work from a different place, maybe, but... Ha, ha, ha, ha. Shall we applaud this boring person that we like so much? By the way, I need to say it. I mean, again, I thank already a lot of people, but I think I miss the two most important people that supported me in these five years four years for you, Christine, and three years for you, Frank. I mean, I think that, you know, again, as we say several times, this is not an easy job, no, I mean. And you have a lot of people working with you, but eventually, you know, when you have to take the difficult decision, everybody say, okay, this is in your desk, no? So you are, you feel lonely many times. And having the comfort of coming at any moment to ask for your advice, and having Frank with his support and friendship throughout these years, I mean, I wouldn't have done it without you. So thank you very much to both of you. Thank you. Thank you. This brings us to the end of this special episode, where we go to hear President Christine Lagarde interview outgoing chair Andrea Ria. You've been listening to the ECB podcast with Stefania Secola. If you like what you've heard, please subscribe and leave us a review. And in the spirit of Europe, I'd like to end in Lithuania today and say, ikki pesimātimo. Until next time, thanks for listening.


Welcome back to theCUBE's coverage of Red Hat Summit, an Ansible Fest happening together, better together here at Red Hat Summit 2023 with Ansible Fest. We've got two great guests here. I'm John Furrier, Paul Gillan. Tom and Sanderson, Vice President General Manager Ansible. Good to see you, thanks for coming on. Good to see you, John. Richard Hensheld, Head of Product Manager Ansible. First of all, welcome to theCUBE and congratulations on, essentially basically being the keynote this morning. So Ansible really mainstreaming into Red Hat's core value proposition. It's always been there, but you guys have done great work, great community. We've been following it obviously with theCUBE. It's mainstream, the concepts of automation, the things that you guys have been working on. So how do you guys feel? I mean, you must feel like pretty pumped. It feel great. I mean, it's really the culmination of about three years of work that we've put into kind of transforming the Ansible automation into a real platform. From Ansible, the community project into a real automation platform that customers can create, manage, and scale automation. Bringing that together with AI now to sort of open up automation to the masses, if you will. Kind of lower the bar to entry for automation means that we can touch a lot more people in an organization. And then having, also having event-driven Ansible being announced at the same time means our ability to take costs out of day two operations is a huge win for us. So I couldn't be more excited. It's like I said I had to pinch myself earlier when we were doing the keynote. Two big announcements regarding Ansible at this event. One is Project Lightspeed. Tell us what that is, and why is it important in the Ansible community? So I'll start, Rich, and maybe you jump in. So Lightspeed is about a two-year project that we've been working on in conjunction with the IBM research team. They brought the expertise around large language models, foundation models. We brought the expertise around Ansible, obviously, in automation. Bringing those two things together to apply generative AI to a very specific use case, which is Ansible Automation, and not just Ansible Automation, but Ansible Automation for the enterprise. And how do we bring that generative AI as opposed to general-purpose AI? How do we really train it and focus it on the needs of the enterprise? Has been huge. And we've invited the community to join with us. As you guys know at Ansible Fast last fall, when we did the kind of preview announcement, we invited that community to come in and participate with us on it. And just think about the principle of what AI is giving us at that point in time. You go, I've got a task that takes a certain amount of time, and I'm doing it manually, and I want to be able to shave bits of time off of that. And that sounds exactly the same as what we've been doing for the past 10 years with server builds, with configuration management, with network, with cloud, with what we've been doing with Edge for the last couple of years. It's just how do we get people to be more efficient? How do we help generate more efficiency and more value out of what people do to give them more time to do more of what they're able to do? And I think the big difference now with the change that's happened with AI is moving away from that, hey, this is, you know, get rid of the person, replace them and do something else. It's like, how do I take that person and supercharge them, right? And say I go from being a job that takes an hour, takes 30 minutes, takes 15 minutes, which means I can do three or four of those jobs rather than just do one of those jobs. And pairing that with the ability to actually train something to specifically help around a set number of tasks. Really, I think there's a real big part of it. I know that Paul's got a question but I want to just follow up real quick on that. So you're saying, and we've been saying in the queue, I want to get to see if you agree with this, humans plus AI is better than AI by itself. Yeah. That you're on that side of... Think of automation, right? I have to, even to use Ansible well today, even as easy as Ansible is to adopt, I have to know what I want to do, right? So even with the AI piece, I still have to know what I want to do. It just helps me get there in a more efficient, faster fashion. So who is the audience really? Is it developers or is there sort of a citizen developer, citizen operations play? Today, Paul, it's taking the existing subject matter experts in our customers' environments, which are, by the way, few and far between. And you guys all know about people talking about the skills gap in IT. So we're not growing a lot of new SMEs. So to make those SMEs, existing SMEs, a lot more productive. But it's also to bring new people into the automation world. Today, like Rich said, if I want to automate a storage subsystem with Ansible, I need to know the Ansible language to write a playbook to do it. And I need to know the storage subsystem and the interfaces that of what I want to do. So I need to know both of these things. If we can lower the Ansible bar, and over time, lower the bar to the domain expertise, then we'll start bringing more and more people into this family, so to speak. Can you share with our audience, because we did cover Project Wisdom, I think that you guys previewed last year. Would I be in research? Which I was, well, love that, love that product. What's changed? I mean, it was like a, it was a coalescing of a lot of things coming together. You mentioned large language models, foundational models, you got computer vision, a lot of great AI, it hit the scene just at the right time. The world spun on the criteria. What did you guys have in motion there? Because I remember, I was asking questions like when is it going to be available, you know, you did a good job of setting expectations. What happened? When was, take us through internally, what was the moment? Was there stings and a snap together? Was there a moment where you say, okay, we're going full throttle, double down? Take this one first. What was the, take us through when that happened, take us through play by play. So we knew we had something before that whole movement happened, right? And we were looking at something, the first time we had a demo, and I think we might have had this discussion last time, it was like, IBM demoed us and they said like, here's, spin up a virtual machine in Azure. And then I said, ask it to do it in AWS.  And that was the moment we were sort of like, that gives us the capability and the power. And then within the space of like three months, the whole world went crazy for generative AI. And then when you look at that, and it's fantastic what they do. I mean, my backgrounds applied mathematics long enough ago, I couldn't have imagined what the stuff they're doing now far more advanced than I can remember. And you see what they're able to do, but right, there's still this thing of, am I getting what I actually need to run enterprise scale in a way that an enterprise needs it to be? And that is automation has to be deterministic. It has to be known. So I want to be able to generate it. I need to know what I'm going to be able to run. And having us go through that motion and be able to get to the point where we can say, specifically, it's getting consistent in its answer. We're making sure that it doesn't do those, what you would say were intuitive leaps in the model to do things that shouldn't be there. A great example I'm using, talking to a customer last month, they were using a general purpose model. They tried to automate some F5, it threw in a restart. You don't want to restart your network when you make a config change. Same time, the actual answer module doesn't have a restart function. So it was wrong twice. It looked right, it looked good, right? So it looks like it was the right thing. That's a hallucination, right? And so you want to be able to say, Ashley, how do I make sure that that doesn't happen? And that's been the focus in the work we've been doing with the community, with the closed beta, with our customers that have been working with us, with IBM Research, with our internal specialists. Having that time and focus of manually sitting there, training the model, validating the model, getting that feedback into it, so that we can actually have that higher level of consistency and deliverability around what content comes out of the system. Because without that trust, how goes at all? Therefore, you're producing YAML code and playbooks using the generative AI interface. So just do that and check. I want to get to event-driven Ansible, because I think for some operations people, that's an even bigger announcement out of this event, Ansible now able to handle streams, big applications in observability. Who are going to be the winners with event-driven Ansible? Yeah, I'll start and then you jump in. So event-driven Ansible, light speed and the AI stuff is sort of the sexy stuff, that gets all the headlines. Event-driven Ansible's what's really going to help operationally today in our customers' environments. And really, that idea of connecting your observability platforms, whether it's for your application or your network or your infrastructure, connecting that directly to automation in a way that, again, kind of superpowers your existing teams as Rich talked about, sort of amplifying their ability and to allow them to take the mundane and the repetitive off their plate by having that automation remediate or take an action automatically and free them up to go do innovation, of what Matt talked about on the main stage today, which was focus on what's coming up next that they don't know yet. So we're super excited about plugging this into our customers' environment.  As you say, it connects to all these different streams, whether they're Kafka streams or whether it's connected right to proprietary products like Dynatriser, Instana or the other ones, it's coming out of the box with this whole ecosystem built around it, so our customers can take advantage of it right away. And I understand you have been testing this in IBM's IT operations. What kind of results have you seen there? Yes, I think that we, the office of the CIO, I think that's what they call it. I think they're called, and so there's about, there's just a large number of them. This is the job they do on a day-to-day basis. They've been off doing this testing. They've been trying to prove how does this take, how does it give me efficiency? How does this take these things out? But we've also, you know, the thing that's been nice about EDA is whenever we talk to people that have, you know, 20 years experience, right? I tried to do this in 2006, right? As a lowly engineer, think I had a great idea with whatever tools I had at the time, and they weren't consistent and all the seniors just said, no, you can't do that. No, it's not possible. And now those same people are sat there and they, we start talking about EDA and suddenly their interest perks up.  It's like, no, you mean I can trigger that from my system over there and that network device can do this and you see their brain spin up and suddenly they start thinking, oh, it's them solving their problems, it's them solving their challenges. It's not us suggesting it to them. We're just extending the capability that we've got. And that's been how most, this has been very organic, right? Since we, I mean, I think the example when we launched, five days later, the first plug-in turned up from one of our partners. Completely unsolicited, right? That was a nice surprise that we'd hoped for, but not quite expected. It's an interesting trend, Paul. We've been hearing about observability connecting into other things. We were at Open Source Summit in Vancouver. One of the hot conversations was Software Supply Chain, SBOM, Software Bill of Materials. And they weren't really, they were being obsolete at the minute they were built because they weren't plugged into the observability. So this dynamic of observability data, I mean, isn't AI ultimately a data opportunity and challenge? 100%. I think you can see down the road as we connect the generative AI stuff that we've done today and you start connecting that with EDA or with event-driven answer down the road where you start having content being dynamically created based on an event structure or a pattern rather than even having to do task automation. You know, I mean, that's, we're not there today, but you can see a point not that far down the road where you start to see playbooks being recommended by AI based on an event type that may not even exist. And that's the conversation we've been having with our observability partners and there's got them, one way they got really excited by this and gave us some really interesting data. So like one of them said, 70% of all changes are what cause outages. All, sorry, outages are caused by social changes. That's the right way around. And anyway, and so I said, well, I think it's, when we have, yeah, AI would have made that, that sentence graphically correct for me. If we had access to the change record that was going on, not only would we have to observe the systems, we'd also be able to see what change happened against them. Well, if Ansible's the change system of record and they're the observability system of record, when we can start sending data back to them, which is our next level of development, they can start doing that correlation. So their AI capabilities around ML and doing all of that correlation around that data, that becomes an important aspect that they can focus on and we're a source of that information to them. But we're also a source of action to them when they want to decide to take some course of action as Tom said with the playbook that's generated. You know, it's interesting, I've been doing a lot of like trying to figure out like a good mental model for AI and you know, humans plus AI, it makes us better than AI by myself. That's what we said earlier. I'll talk to a friend and he's like, he's into chess. And he said the, there are more grandmasters in chess when they allow AI, augmented AI, humans playing against humans with AI. So if you machine against machine, we know what that looks like. A person against a machine, we know what that looks like. But a player with AI, there's more people who weren't competitive by themselves with AI, makes them more competitive, which brings up the point of chess is kind of like declarative, you know, you mentioned that. This is the value, it's the humans. This whole job loss thing is kind of just a fake story. That's just kind of fear mongering. But there is a real human opportunity here to leverage the AI. What's your philosophy on that human role with AI? How do people harness it? Because they use words like training data. I think of a pet when I hear training. It's like, I train my dog. So is AI going to be a pet? Can AI train my dog? I need to train my dog. I mean, people have to embrace AI.  Some are jumping all in. Yeah. Look, let me make a point on that because it's one of the areas that we focus on, which is obviously our customer base, our enterprises, government agencies that are really cautious and careful about making sure that what they're doing in their environment is not going to put them at risk of some sort of issue. Let's put it that way. And so the idea here that we give them enterprise guardrails to guide how their users use AI with Ansible in their environment is a very powerful message. Me just entering in a question into an AI platform. If someone's listening to that question, they can immediately see what are the areas that I'm working on. If I enter some question about configuring firewalls, they know I'm working on something with firewalls. That's already a pattern that someone could observe and say, hmm, Tom's Bank is trying to reconfigure firewalls. Maybe that's somewhere I ought to go exploit. So our ability to deliver AI in a purposeful way to the enterprise and be able to have them implement policies on how it's used and consumed, I think starts to protect a lot of that sort of human fear of what's going to happen in the environment. It was the same argument for automation. 2012, 2013, when we first started getting into that, automation was really possible at the scale beyond what was available for. And everybody was hesitant to say, why would I do that? That will automate the amount of a job. And those things didn't happen either. And we're busier than we've ever been. There's more complexity than there's ever been. We're doing more work. There's more work to do than we've ever had to do. And at the moment, we're still churning manually We see how much burnout is there as a new trend in the world, from a wording perspective, we've all known about the stress and everything that comes from it. So why wouldn't we use it to alleviate some of those things? And so I understand there's a negative nature of people who are going to go to that thing, but we also have to remember the positive that will come. Well, they were negative on the web. Oh, that's just a toy. It's so slow. Yeah, well, it's not the real internet. What's the next frontier for AI as it applies to Ansible? I think what's going to get really interesting is when we move to bring EDA into the AI space, and then, you know, automation has to be deterministic. So therefore, as I said before, we have to have that ability to say it's the same piece of automation. Once everyone gets run time and time again, once I have, I know what automation I've got and how many of them I have, well, then I can have the AI also makes those suggestions about which bit to run, which I think ties into the observability. It ties into that ability to say, I can react quicker, right? I may not react for the entire way. I may just be able to react quicker than I could do. That meantime to recovery, the ability to, I was talking to a customer this morning and saying, you know, it takes six hours for us to resolve serious incident calls. Well, three hours, two hours, one hour. Maybe not even having to have the serious incident call because we knew what we were doing. I think when we can combine those two things together further down the path, I think that's going to give us a really interesting opportunity. Thomas, Richard, thanks for coming on theCUBE and we should do something after the event. Get more content from Ansible's community out there. Maybe do it asynchronously on theCUBE remotely. But I really appreciate you guys coming on. I'll give you the final word. Speak to the folks out there in your Ansible community about Ansible Fest this year. It is part of Red Hat, so it's going on here for the folks that didn't make it or didn't know about it. Give them a message about what's happening and what they can do to connect. Yeah, no problem. So yeah, Ansible Fest at Summit this year has been a great opportunity for us to bring more people, expose Ansible to more people. We had a contributor summit or a contributor day, the community day on Monday yesterday, and it was triple the size that it would normally be at a regular Ansible Fest. So this opportunity for bringing more and more people into our community is a great thing. And as usual, we always invite people to community to get involved in the new Ansible projects we're working on. Ansible Lightspeed, a venture of Ansible, those community projects are growing by leaps and bounds right now. So we welcome everybody to get involved. Great, congratulations on your success, continued success. And looking forward to chatting, chatting more later. Thanks John, thanks a lot very much. I'm John Furrier with Paul Gillin, The Cube Coverage, day one of two days of coverage of Red Hat Summit and Ansible Fest here in Boston, Massachusetts. We'll be right back with our next guest after this short break.


Ahoy, ahoy, and welcome to the channel.  SCP-173's image is finally being taken down. Let's talk about that. So about two days ago, somebody on my server alerted me to the existence of a thread on the O5 command forums, which was essentially beginning the process of taking down SCP-173's image. Now, SCP-173 for the six people who maybe don't know, is the original SCP article. It's the one that was posted up on 4chan, and then eventually made its way over to the wiki. It's the original. All other works essentially sourced from it. So what we're dealing with here is sort of a titanic shift in the history of the SCP wiki, but a necessary one. Now, the, hmm, that's the best way to put this. The image's policy on the SCP wiki requires that every image posted be Creative Commons compliant, that it's compliant with the site's license. And over the last fourish, is it fourish? I think it might be fourish years, maybe a little bit less, maybe like three. But over the last three to four years, there's been a policy in place that I originated, but we'll talk a little bit about why that doesn't really matter anymore, that I originated, where the articles, there was basically a process for taking stuff down, but we never really got around to the idea of what we call legacy images, stuff that was posted before the policy was put into place. And I say, part of that doesn't really matter, because I instituted the policy and then completely disappeared, well, didn't disappear, I resigned from staff, and then left all of the really hard, really extensive work to other people to complete. So I don't really think I should get very much credit for that, but I did institute the original policies, and so this is something that I've been waiting for for quite some time to actually be completed. This is the last of the legacy images. So our policy is any new posts, they have to be Creative Commons, but the old stuff that was posted before the policy was implemented was sort of being dealt with one by one by one. SCP 173 is the last image on the SCP Wiki that is not Creative Commons compliant, I should say, the last old image that you could still post something today that isn't Creative Commons compliant, it'll just get taken down very shortly after. So this is the last legacy image, this is it. Once we do this, that's it, the SCP Wiki is completely Creative Commons compliant, and if they ever decide to go to their own website, then they don't have to worry about the legal implications of having non-Creative Commons images on there, and that's actually super important. We're not going to go into why that's super important, but it is super important that, like I said, if they go to their own website, that they run, own, pay for, and all these other things, and they allow, knowing that they're not Creative Commons, they allow images on their website, that's a problem, and then they don't have a process to get rid of them, which they do now. It's great. I'm actually quite happy about this. I've been wanting to see the SCP Wiki go completely Creative Commons for a really long time, and they're finally getting there, and let's talk a little bit though, because this is important to people, not just the process or the reasons why it's good, but the more personal nature of SCP 173 for a lot of people, and even for people who maybe think that SCP 173 is a poorly written article, which it is, but it doesn't matter because that's not the point of it. It's not supposed to be great art. It has inspired some great art. I think one could say without being too pretentious about it, not everything on the SCP Wiki is amazing, but some of it's very, very good, and it was inspired by SCP 173, so we got to give it that credit. The original image, untitled 2004, was created by Izumi Kato, who was not given any sort of notice or information that this was, his stolen image was being used, and didn't really find out about it until it started becoming particularly popular. Now, a lot of people in their minds, especially since a lot of us are creators or want-to-be creators, would think themselves that the only thing that matters is exposure. The more people that see your stuff, the better, right? But that's not the driving fundamental creative force for a lot of people. For some people, it's just that their vision is communicated properly. And while I'm not necessarily disagree with that from a personal standpoint, that doesn't mean I don't respect it in other people and their goals in creating art. So just because I've seen this already, he should be, and this is in the past, this is today, he should be happy that we are allowing it on the website to show to so many people. He should be happy that his art is being shown to everybody, despite the fact that he didn't want it. He very begrudgingly allowed it on the SCP Wiki. Let's be clear about that. While he did accede to the SCP Wiki using it, and under very restrictive circumstances, it was a very, it was a begrudging allowance. Mainly on the sense of, there's this, hey guy, there's this whole community that sprung up around your image has nothing to do with the art that you created. Uh, can you please let us keep using it? And the guy's like, I guess, just don't sell it. And a few other things. And that's not what you want. You don't want to be guilting people into allowing you to use their content. A yes is not, you want enthusiastic consent if we're going to use the right terminology for it. You don't just want, yeah, I guess, you want, yes, I'm very excited to see this be this way. And that's not what we had. We haven't had it from the beginning. We didn't have any consent at the beginning for using the image.  I guess keep using it. And that's not respectful to the original creator, right? If we think of SCP 173 as this fundamentally important, if you really think it's irreplaceable, that's actually sort of an argument for replacing it or getting rid of it. We'll talk more about the lack of replacement in a second. It's actually sort of that because what you're saying is, really, is that you have gotten so much use out of this image.  Only what I want matters. It's very important to me and I'd like to keep it. What you should be thinking is, is thank you, Azumi Kato, for letting us use it for as long as you have. That's what you should be saying. And we're going to be more responsible and more moral and more ethical in the future with regards to our images. And that starts by giving him back his art, which is what we've done, hope, or we're going to do. There's really only a discussion going on right now. It's probably going to go in the right way, but you never know with these things. The original author of SCP-173, the article, has asked that the image not be replaced. And to that end, it seems very unlikely that it will be replaced. There's some people that have just been like, eh, screw what the author wants. We'll do what we like. But of course, that's the exact same attitude that got us into this problem in the first place. So that seems kind of silly to me. And there is going to be a couple of weeks before they even get around to voting on it. And when they vote on it, then they'll take it down. It's not going to be replaced. There is some talk about a possibility as of creating an SCP-173 gallery to allow for other interpretations of SCP-173.  But at its core, this is the right thing to do. And this is well, well, well past the right time to do it. If you have any opinions on what you think should be done with the SCP-173 now, let me know in the comments down below. I know a lot of people will be talking about the possibility of a gallery. Or I'm sure plenty of people will be like, leave it up. This guy should be appreciative that we put it on our website and made his stuff so popular. But, you know, whatever. It makes me so angry. I saw that. I saw somebody actually saying that on a Facebook group recently and be like, oh my goodness. It's like rocket at the end of Guardians of the Galaxy. What if I steal something? That's theft. You can't do that. No, no, no. What if I want it more than the guy that has it? Well, it's still theft. No, no, no. I don't think you understand, sir. What if I want it more? It doesn't change anything. Just because you want it more doesn't mean it's your property all of a sudden. But yeah, that's the end of the video. I just, I'm in a pretty good mood over this. But thank you very much for watching. If you enjoyed the video, hit the subscribe button and then hit the notification bell. And then head on over to patreon.com. I actually lost our $50 patron, MC Cashmull, who is, I would like to say thank you very much for pledging for as long as you have. That was really cool of you. And I know everybody's got their own reasons for why they can and can't pledge. And I completely and totally respect that. But I thought it was worth saying thank you to MC Cashmull. And I think anytime I get one of those big patron backers, I should probably thank them when they come in and I should probably thank them when they leave. Yeah. And Synderiki, who's pledged $100. So I don't think I've thanked him enough, but I'll say it again. But everybody else here on the screen, you could join with them at $1, $5, $10. There's a $1 tier. Just doesn't show up in the little table here. But it's nice to know that I'm not alone out here. And I will see you all again on Thursday. Took me a second to remember the day.


Hi everyone, my name is Steph, this is Little Bookish Teacher and today I want to talk to you about The Kindest Club by Kate Bullen-Cassanova and Dave Petzold. This is a picture book that was published by Hardy Grant in November 2022 and it is an absolutely gorgeous picture book that is really perfect for three years old and up and it follows a group of children who are probably in preschool or kinder so it's perfect for that age group but you could also definitely use this in early primary school if you wanted to. So The Kindest Club is a book that goes through the kinds of activities and experiences kids have in preschool or kinder but it does challenge gender stereotypes in the types of activities that boys and girls like which is really fantastic to see. The entire story takes place through the course of one day and so you get a real sense of time as we move from activity to activity from morning to lunchtime to the afternoon and it has a really gorgeous rhyming narrative style to it. It also has a really great diverse cast of characters including cultural diversity and kids with disabilities. What I also love in that sense is how it depicts the space as being set up for easy access for all of these characters which is not always something that you see particularly in detailed background images. This is one scene I'm thinking of where you see ramps actually included in the space. There's also a lot of different emotions depicted in this book from nervousness and apprehension as one character starts at the school to joy and sadness and everything in between. There's also really great friendships and how kids help each other out when things don't always go to plan in this book which was just beautiful to see and mostly it just has really fun bright beautiful illustrations as well. This was a book that I received in the little book rooms quarterly picture book subscription. I hadn't heard of it before then and I'm so glad that I ended up with a copy of it because it is absolutely gorgeous. Who doesn't love something that celebrates how kids learn and explore and grow and develop friendships? I mean it's just a beautiful beautiful book. In the description I will leave a link to where you can find the kindness club as well as any links to the author's websites or social media. In the comments I'd love to know if you've read this book or if you're planning on picking it up or if you have recommendations for similar books as well feel free to leave them down below. If you want to let me know that you're here but you don't want to leave a comment feel free to leave any kind of toy emoji in the comments otherwise I hope that or if you're on the world you're staying safe and healthy and I will see you in my next video. Thanks so much for watching. Bye everyone.