So, you can divide these amino acids into different classes. So, these are my 20 amino acids glycine, alanine, serine, thionine and so on and so forth. You can sort of characterize the fundamental properties. So, one class of amino, so it is made up of carbon, oxygen, nitrogen, hydrogen, sulfur. This over here is my side chain, which so this is the fundamental the main chain and this is the side chain. The side chain distinguishes the different amino acids. There are few classes. So, one class is the hydrophobic amino acids, which do not like to interact with water. So, whenever you have hydrophobic amino acids, they will try to tend to cluster together. You could have hydrophilic amino acids, which like water and therefore, if you put in hydrophilic amino acids, they will try to sort of stay away from each other and close it to water. Similarly, you can have charged amino acids and I will upload them which ones are charged and which ones are not. So, you can take these amino acids and join them together to form what is the protein backbone. So, here is my sequence of amino acids that forms this protein backbone. Given a sequence, the protein can fold in different ways. So, that first level what is called the secondary structure, you can have alpha helices and beta sheets depending on the sequence of amino acids and whether correct hydrogen bonding can form or not. So, you have this alpha helix and the beta sheets. So, these are called the secondary structures. You can have a higher level structure which is called the tertiary structure. So, for example, these you have alpha helices over here which coil amongst themselves. So, this is for example, the hemoglobin protein. So, depending on how it folds, so this is the protein folding, it determines its function for example, the hemoglobin forms a pore where the heme atom can come and bind. And then there is the quaternary structure where different polypeptides can come to. So, hemoglobin contains four of these heme oxygen binding sites and together you have this hemoglobin protein and there are an immense number of them. So, let me that is all I want to say as far as proteins goes. So, you have the primary sequence for proteins. So, the sequence from there on you have the secondary structure which are alpha helices and beta sheets and from there on you have the tertiary structure which is actually the folded structure of the protein and so on. So, when we go on to when we talk about protein folding later on in the course, we will sort of use these properties. So, again protein folding is sort of an open question right. So, that is one of the most common themes that I will tell again and again in this course. No question in biology is completely solved, everything is an open question right. So, that is why it is nice because you have lot of areas in which you can contribute, but nothing is completely understood. So, protein folding is of course is one of the canonically hard problems to solve, but the way one at least starts to think about them is to try to group these amino acids depending on their properties depending on. So, the simplest class of modeling depends on whether the amino acid is hydrophobic or hydrophilic or charged and depending on that you try to build a model which are called these HP models of Dill and Thomas and company. So, anyways I will talk about that when I get to protein folding at least a little bit about that not a lot I have another video. So, here is the sort of central dogma what I was talking about the transcription translation process it is actually a fascinating thing. So, even if you know or if you have forgotten it is nice to know once again. So, here is my DNA strand right it contains this A T G's and C's I have this machinery of proteins that comes and binds to this DNA strand. It unzips the DNA strand which means it opens up this double helix and makes a single strand ok. So, once you have made a single strand this machinery sort of zips along this DNA and it reads off this basis and it produces the M RNA. So, this yellow thing is my M RNA which is being produced. So, let me pause once I got whatever. So, here in this DNA it is where you have this A T G's and C's on this yellow ribbon which is my RNA you have this A U C G's and C's ok. So, that is where the uracil comes in. So, it produces a copy of this DNA in this RNA molecule ok. Now, the DNA contains introns and exons which means that only some parts of this are required to actually produce a protein. So, so gene so I have this sequence right A G T C C G whatever it turns out that when you are producing the protein not this. So, it starts off at let us say this A U G's since that is what I said and that is my start and it ends somewhere at an U A A which is one of my stops ok. So, that is that this whole thing is this yellow thing that is going to be produced in the red by one M RNA ok. It turns out that not all of these are required to produce the protein. You have regions which are not required which are called the introns and regions which are required which are called the exons. So, when the M RNA is produced it has both these introns and these exons. The cell then snips off this parts which are not required. So, here for example, this green parts which are the parts which are colored green are the parts that are not going to be required for producing the protein. So, what the cell will do is that will splice off those parts using another protein machinery which is called the spliceosome. So, here this thing the spliceosome will form it will bring together the two ends of this green strand and it will cut them off it will join the yellow parts to one another. So, that the exons are connected together ok. So, it is a fascinating thing how the cell does this sort of reproducibly again and again and again and again we do not understand the complete mechanics of this. So, it will splice off that green part at some point and join the exons yes that is it. So, it will do that over the length over the length of the gene. So, gene typically will have many introns and exons. So, it will strip off all of these green regions and get you a continuous yellow strand which is all the coding part of the gene the green parts the non-coding parts ok. So, all those green parts have now been cut off and you have this polymer which is like this whole coding yellow polymer. So, this yellow polymer is then ejected out from the nucleus. So, it comes out of the nucleus out in the cell cytoplasm you have this another protein machinery which is called the ribosome. The ribosome then reads this mRNA this messenger RNA this yellow thing that came out and it constructs the protein. So, these green things are transfer RNAs which are bringing these amino acids corresponding to these sequences and as it brings in these amino acids it constructs the protein one by one by one or codon by codon by codon and this red thing comes out which is your protein this red thing will ultimately fold and form the tertiary structure of the protein. So, this is the central dogma transcription DNA gives rise to RNA gives rise to proteins. So, this transcription translation process. So, here is the protein molecule coming out of the ribosome and will fall fold into whatever structure it is supposed to fold in for its particular function. And the same ribosome will sort of read any different RNAs and produce the right protein that is encoded in the sequence in the mRNA here or when in the first part or in the second part not here this one plate those green things may be those are the transfer RNAs these things right. So, these are the transfer RNAs this tRNAs contain an amino acid. So, they will have let us say a methionine or another tRNA will have let us say glycine and so on. So, corresponding to each amino acid you have this package which this transfer RNA which will bring this correct amino acid ok. So, once it brings it into the ribosome the ribosome takes off this methionine and it releases the transfer RNA back. So, these are sort of your Amazon deliveries which are bringing the right package. So, that you can incorporate them according to the sequence into the protein right. Now, as I said so, this part at least we understand how this happens the DNA produces RNA produces proteins, but like I said the cell contains many, many genes right genes are regions of the DNA. So, what is a gene? A gene is a region of the DNA which encodes for a protein region that encodes for a protein that encodes for protein ok. So, of course, you have many, many millions of genes it is not necessary that all of these genes are going to be active at the same time right or indeed active in the same cell type. So, how these genes are expressed itself is a field of study. So, these are experiments done on DNA microarrays where you so, each spot in this microarray is a single gene and then the intensity of this fluorescent spot tells you how active that gene is. You can do that so, you can do this so, this is this is a black and white microarray which you can convert to a color and then you can do that over a period of time. So, this axis is time and you can do that for different genes. So, in this picture over here my pointer does not work, but in this long picture over here the x axis is time the y axis is different genes ok. So, different and I think if I remember correctly green is sort of strongly on and red is strongly off I could be mistaken it could be the other way round. So, you will see that a gene stays on for some time then it switches off and then again it turns on back at some other time something stays off it switches on then again turns off and you can look at how these how these gene expressions are correlated in time. So, which genes switch off with what other genes ok. So, you can build these correlation matrices among genes by looking at this sort of gene expression data as a function of time and that again tells you functional information about how genes cross talk with each other how different proteins interact with each other. So, if one protein is interacting with another protein those two genes might be turned on at the same time or they may be turned off at the same time. So, these are enormously complicated, but again this data is available we do not exactly completely understand this data of course, but at least as far as modeling purposes goes a lot of data is available. So, again this gene expression omnibus is again an open source genomics database where you can go in and you can see what sort of this gene expression data is for different organisms for different cell types are available and again these are slightly older numbers I am sure the numbers have sort of improved in the time since I took this slide alright. So, the idea. So, now that I have talked a little bit about biology so, what I hope to achieve during this if you to buy this course is that we cannot cover all of this biology that is a given biology is enormously complicated I will cover may be 5 percent right, but what I hope that you will take away from this course is an idea of how should you given a biological problem that you are interested in how should you go about building a model that takes into account the quantitative data that is available and makes quantitative predictions ok. We will do different systems, but hopefully the idea the ideas that will develop what will be applicable across to whatever other system that you might be interested in. So, the again the generic idea is that of course, you so sort of try to identify which parts of the system are relevant to the question that you are asking and then you overlook the other parts the other complexities and you develop a mathematical model and through that model you generate quantitative testable predictions right. What should this model satisfy? Of course, it should satisfy basic physical laws just because biology is complicated does not mean it can violate physics. So, you will have whatever conservation laws you will have whatever laws of thermodynamics that we know. The key thing is to sort of identify what are the relevant features for the question that you are trying to ask. So, if you are trying to ask about this chromosome folding does it really matter what exactly the sequence is maybe it matters maybe it does not I do not know. You might say that well because this is this is at a scale which is one third of a nanometer and this packaging is at a scale which is of a micrometer maybe the exact sequence details do not matter. You may be right you may be wrong I do not know, but that is one of the key things that one needs to learn how to identify that what are the relevant features what are the relevant spatial and temporal scales that I should be talking about. Once you have identified that you can guess what is the correct physical description and write down these mathematical equations. Once you have a mathematical equation you can generate predictions and then if you are right great if you are wrong you tell yourself that you learned something which is true and you go back and generate a new better model hopefully. Just to show you what I mean by this identifying the correct scales and the correct features of the problem let me give one or two examples. So, here is DNA right which we have been talking about. So, at the very microscopic level if I go here closely into the DNA what I have is the sequence right over there in the top and you might. So, if you are asking questions at a very microscopic level that will a protein come and bind here or there that depends exactly on the sequence of the DNA. So, you need to know the DNA you need to know the DNA sequence if you need to make any predictions of that sort. You can maybe abstract a little bit and say that well I will just abstract this sequence into two types of sites a binding site or a non-binding site for the DNA and then this protein might have different maybe it has thousands of binding sites over the genome. So, instead of saying that it is A, T, G's and C's I will just convert this into a binding site a non-binding site a binding site and so on. That is another level of that is another level of abstraction, but if you are just interested in how much a protein will bind maybe that is enough. If you zoom out a little more if you are not interested in such granular microscopic details you could maybe treat the DNA as a charged rod. So, these nucleotides have negative charges exposed. So, you can treat the DNA as a negatively charged helix double helix or because it is a very long object you could treat it as a sort of elastic rod. So, beads connected by springs and then you could ask how easy is it to bend the DNA. So, for example, because you have to package the DNA inside the nucleus of course, the DNA cannot be very stiff you need to be able to bend the DNA like this, but at what scales can you bend it. So, what is the persistence length of the DNA questions like these maybe you do not need to know the exact sequence maybe you can just model it as a bead and spring and then depending on the stiffness of the spring or if you get the right stiffness you might be able to capture the correct physical properties of the DNA. If you zoom out even more you can maybe talk of the DNA as a random walk in space and that would get some things right that would get some things wrong. So, even if you think of DNA as a random walk it is not that that is a completely. So, random walk in space it is not that that is a completely bad model let me just show you this. So, some of you have done random walks right. So, how does the end to end distance scale for a random walk with the number of base pairs or with time anyone. So, if I have a random walk made up of n. So, n legs so, 1 2 3 4 like that then how would the end to end distance scale with the number of units r square would go as n to the power of 1 right. So, if you look at various DNAs for various organisms. So, this is the radius of gyration which is closely related to the end to end distance you will see that it grows as n for various different organisms. So, this is a virus lambda phase this is east this is a fly chromosome and then there is the human chromosome over there if you connect all of these dots. So, if you just say that if I take this DNA polymer and I ask how large is this polymer can I get an estimate of the size of this polymer given the length of this DNA which is n it turns out that r g square goes roughly as n and that is true across organisms. So, depending on what the question that you are interested in different levels of modeling may be appropriate. You need not have the exact microscopic details in order to ask a very macroscopic coarse grain question. So, you need to identify what spatial scales or what temporal scales are important in order to answer the question that you are asking and then do your modeling at that correct scale. So, this is true I showed for DNA this is also true for proteins. So, again at the most microscopic level you have your amino acid sequence which is whatever amino acids that you have. You could then sort of abstract away a little of the complexity and say that I will just say whether it is a hydrophobic molecule or a hydrophilic molecule and then you come to these class of models that are called this high HP models. You could abstract away a little more and say I will only talk about the second V structure whether there is an alpha helix or a beta loop or you could just say that well I will just talk about the functionality that it is may be binds to some other protein. So, it is acts as a receptor or the ligand and then I can just talk about the energetics of this binding this ligand receptor binding and again these are all of these are things that we will sort of look at in greater detail as the course goes on. But the idea again is this that the most crucial thing when doing modeling of a biological problem is to sort of identify which features are important to that problem and which features are not. If you can correctly identify that you can then go about building the correct model for your problem. So, we sort of think of biophysics as a slightly modern subject in some sense it turns out that you know people have been thinking about it for a long time. For example, Schrodinger of quantum mechanics in his what is life book said that thought about these same problems and sort of crystallized the question by saying that these are events in space and time which are taking place within the boundaries of a living organism. And how can I account for these processes that taking place in space and time which cannot violate my laws of physics as I know it how can I account for these processes using the tools of physics and chemistry ok. So, that sort of the spirit in which we will approach this course that how do I take this complex processes that are taking place inside living organisms and how do I account for them using whatever physics knowledge that I have or whatever chemistry or maths knowledge that I have ok ok. So, that is yes that is all for today so we will reconvene back here on Friday.


Well, we're still talking about defining. And we had different kinds of definitions with the last chapter, or the last video I should say, same chapter, the last video. And that's all well and good. But you can still provide a kind of definition, but not do a very good job. And when you're providing definitions, there are still rules you have to follow. You can't just stipulate anything as a definition. So here's my definition of square. It's something. No, that's not going to do it. You've got to follow some steps. You've got to follow some rules to provide a good definition, whether it's through extension or intention. So the first rule to follow is to define literally. Now what this means is to define literally as opposed to say something metaphorically. If you're going to define, define in terms of what's true or false. Not, you know, artistic expression.  I'm not. But I will say this, if you don't understand the literal definition, the artistic definition or the artistic expression isn't going to do you much good. We want to talk about what's true or what's false in terms of what's true or what's false. We need to talk about metaphor, or, you know, most of artistic expression. Talk about what's, you know, literally false, right? So what? Democracy. An artistic or metaphorical definition of democracy is what? The hope of the common man. I mean, maybe, kind of, you know, it's not literally true because there's lots of hopes for the common man. Or, I don't know, maybe lots of ways we can start defining artistically democracy. But instead, we want to give a literal definition. We want to say what's, you know, what's true or what's false about democracy. So what? A working definition of democracy is probably a little bit rough, but a former government where laws and decisions or policies of the government are decided upon by popular vote. It's kind of a stab at a working definition of democracy. I'm sure there are political science majors out there who will correct me very quickly. But, you know, that's something different than the hope of the common man, or government led by the blind, or, you know, there's all kinds of ways we can start artistically describing democracy. We want to avoid that. We want a literal definition as opposed to metaphorical. The next rule of defining that we have is to have a cognitive definition as opposed to an emotive or an emotional one. That means, you know, trying to keep the emotion out of it, right? We want to describe what the thing is as opposed to how it makes us feel, or how we're trying to make the other person feel. So, you know, sticking with the definition of democracy again, right? A former government whereby decisions, policies, laws are determined by popular vote. Okay, you can throw some emotionally charged words in there, right? So the former government whereby most important and vital decisions of society are left up to the ones who are least qualified and least trained. You know, it's kind of a pessimistic view of democracy, or maybe another version. The former government whereby the most important decisions are determined by those who have the greatest stake. Okay, I mean, these might both sort of be literally true. Okay, but they've got lots of emotionally charged words loaded in there. We want to avoid emotion when we're defining these things. I'm not trying to say emotion is bad. I'm not. Emotion has its place. Emotion is good, right? But when you try to reason, you know, these kind of emotionally charged words can really make things more difficult.  In fact, being emotionally charged tends to inhibit one's ability to comprehend what's going on. Just get too wrapped up in how you feel about it. So second rule, a cognitive definition as opposed to an emotive one, or emotional one. The third rule for defining is that the definition should be referential as opposed to self-referential, right? So when you refer, you refer to something else. You're defining in terms of something else. A self-referential definition doesn't tell you anything. So when we talk about a democracy, when we say it's a former government, that, you know, or decisions, laws, policies are determined by popular vote. Okay, well, nowhere in that definition did I use the word democracy again. I've defined democracy in terms of something else. A self-referential definition would be something like what? Democracy, well, it's a democratic government. Well, yeah, but that doesn't tell anything. When you define, you shouldn't use this, you know, the term that you're trying to define, right? The definine should not be the definindum, right? So yeah, this rule. When you're defining, defined, or referential definition, defining in terms of something else, rather than that one thing. All right, so the next rule of defining is that definition should be precise as opposed to vague. A vague definition includes too many things. A precise definition narrows it down. So let's return to our little example of democracy. And a vague definition of democracy is a former government where decisions are made. That's way broad. That's way too broad. One way of thinking about making definitions very precise is, say, let's deal with the terms of lexical definitions, where the genus and the species. Well, you know, a very vague definition wouldn't have a very good deferentia, right? So a system of government. Okay, well, that takes care of the genus. But then a vague definition wouldn't separate that definindum from other things of its kind. So just broadly speaking, in terms of governments, we have what? Government by the many, right? Democracy. Decisions are made by popular vote. You have what? oligarchies, where decisions are made by very few. You have a dictatorship or monarchy, right? Where decisions are made by the one. And there's even, you know, versions, there's even differences between, say, dictatorships and monarchies, right? We can talk about that too. So you've got all these different kinds of governments. Well, a vague definition wouldn't differentiate between them, right? You want to define a democracy, we want to make sure we narrow that down. So when you're defining, make sure your definition is precise. It doesn't include too many things, right? As opposed to vague. Vague definitions include way too many. They don't have a good enough differential. It's maybe a way of thinking about it. Defining univocally. Strictly speaking, it's probably not when you're defining that you need to keep track of this term. It's when you're reading or writing. So univocally means that you're using one defininums for the defininum. And believe it or not, it's really easy to start swapping out defininums unconsciously. Now this is largely going to take place in a paragraph or a paper when the author, it's mostly unintentionally, author will start using a different, different irons at one point than another. So we could talk about democracy as a popular form of government. We're all a fan of democracy. Therefore, we're going to be a fan of the Democratic Party. Well, I mean, it's true that the Democratic Party is in favor of democracy, but yeah, that doesn't really distinguish Democratic Party from, you know, Libertarian, Green Party, you know, Republican Party really doesn't distinguish. They all want to appeal to democracy in some way, shape, or form. And when you're reading a passage, you want to be careful about, you know, want to keep your radar off or when people unintentionally and just kind of quickly slide into a different defininums. So you can have a Democratic former government not necessarily have a Democratic Party or the Democratic Party, right? So these are two different things. You're at the definition of democracy is different than the definition for the Democratic Party. That's just one example, right? You could be a favor, you know, you could say, well, republic's a good thing. Therefore, I'm in favor of the Republican Party. Well, no, I mean, this, I mean, that's fine. You could be in favor of both, but it's not like the Democrats are necessarily the Democratic Party is necessarily trying to get rid of the public. They're not trying to do that. So you got to be careful when you're reading and when you're writing to keep track of one defininums for the definitum, to use the defininums for that word, for that term, once and stick with it with the entire paper when you're writing. And when you're reading, keep your radar off for when people kind of accidentally slip into a different term. And I said this is this rule defined univocally as opposed to ambiguously. By the way, ambiguity and vagueness are two different things. Vagnus is when your definition is too broad and includes way too many things. Ambiguity is when you kind of switch definitions, right? You have two different definitions or more for the same term. So define univocally as opposed to ambiguously. All right, last rule for defining. Define affirmatively as opposed to negatively. Now I'm not talking about emotions again. We already talked about defining cognitively as opposed to emotively. What I'm talking about here is defining in terms of what the thing is as opposed to what the thing is not. Now I know, I know we just got finished with a kind of defininums called exclusion. And we had antonyms and we had species similars. Right, that's not a defininums according to this rule. You're right. Now if you recall, I said we need to define it by exclusion to define not just by exclusion. You know, it can be helpful to start with an antonym. It kind of works like a synonym, really. In some ways, and it's not always useful because there are not always antonyms for every defininum. And species similars, again, you're not defining in terms of species similars. Although, you know, related to ambiguity earlier, having, you know, having talking about this defining by exclusion with species similars right from the beginning to remove ambiguity right from the beginning, that can be helpful. Right, that can be helpful to say, I want to talk about this thing, not these other things which are often used for this right here. But it's strictly speaking is not a definition. If you just define by exclusion, you probably haven't done a job. Probably haven't defined. In fact, I'll be willing to bet that you haven't. So when you've defined, right, defined in terms of what the thing is. So, you know, in the case of democracy, right, you know, we have, well, you say it's not, it's not governing by the one. Right, that's a start, but that still doesn't tell us what democracy is. Even if you say it's not rule by the one, it's not rule by the few. Right, there's still more to go. There's still lots of different ways you can have a government. Even a government through more or less popular vote, maybe if you like restrict the number of people who can vote. So only landowners or only people who, what, only people who live in a particular area or something like this. Right, there's all kinds of ways we can still have rule by more than a few people and still not be a democracy. So defining by, only by exclusion doesn't do it. You've got to define affirmatively, you can define in terms of what the thing is. So define again, a form of government through popular vote. You know, a working definition, there's probably better definitions, but a working definition. That tells us what it is as opposed to what it's not. Well, it's not an oligarchy and it's not a monarchy. There we go. No, that doesn't do it. So that's, these are our rules. Define literally as opposed to metaphorically, artistically. Define cognitively as opposed to emotively. Define referentially as opposed to self-referentially. Define precisely as opposed to vaguely. Define univocally as opposed to ambiguously. And finally define affirmatively as opposed to negatively or negation. Now, so I want to point out, right, it's possible to define in terms of one of the kinds that we talked about with the last video. You can have a lexical definition that is vague. You can have a synonym, right? That's what? Emotive or metaphorical, sort of. So when you're looking at these definitions, not only when you're defining, right, not only to make sure you stick or you try to not stick with, but you define as well as you can with all the different kinds of definitions, but make sure you stick with these rules. You've got to follow these rules. So when you're reading passages, keep an eye out not only for the kinds of definitions, but you want to make sure that these definitions are adhering to these rules. If they're not, right, there's a problem here. There's a problem with comprehension of the terms. And if you got a problem comprehending the terms right from the beginning, that can and probably will mess everything else up. So keep an eye out not only for kind of definition, but whether they appropriately follow the rules.


What is going on guys welcome back to the channel critical overload here so am I howling ends in this video here again today talking about? Nick Castle once again I know I just did a video in in that compilation video or that I don't know what I'll call it the video where I did multiple updates That's what I'll call it So talking about that really quick before I jump into the topic of how many deaths will Michael Myers even have in his movie Nick Castle again put out a tweet acknowledging that he's work doing some final ADR work for the very last time as Michael Myers We know that Nick Castle has been starring in both of these movies from David Gordon Green doing cameo work James you Courtney has been the one dominantly starring as the shape Michael Myers But it's been nice to have James you Courtney who we not James you Courtney's it's nice to have Nick Castle Who we know has been Starring in these movies as cameos and he was the original Michael Myers in the 78 movie It's been nice to have him back and it's nice to see that he is getting some ADR work finished We know that he again will have a cameo in some capacity going into this one I've already stated. I think he might show up as a hobo But he could show up as some other figure in Haddonfield and the most of the Halloween Kills or Halloween kills 2018 Michael Myers predominant work. You've seen will still come from James you Courtney so jumping into another topic of How many kills will Michael Myers actually have I know a lot of people going into this movie will be like if there is Not the same amount of kills that Michael had in Halloween kills, they're fine with that But I think what people might not be okay with is if a chunk of the kills that are happening in Halloween ends Come from Corey Cunningham, and I will say this I am already gonna go into it with that expectation only because that just seems like a safe safe thing to just kind of prepare yourself for Not that who or who's doing the kills is that big of a deal if the course the overall movie is piss poor But Corey Cunningham Considering that he is being shaped to be this new focus kind of like an obsession in Haddonfield They view him as this new boogeyman They're kind of seem to be glimpses of maybe we consume their bullying him This will lead to him dawning the Michael Myers mask going off of again The fact that we see what we think is Michael Myers with all their fingers back on a hand where they shouldn't have all their Fingers at one point we see the shape wearing a ring So many things we see Corey actually at one point in the sewer getting grabbed up by Michael Myers So it's safe to assume that at some point He snaps puts on the mask after an encounter with Michael and he hacks off a few people who were bullying him and mistreating him in Haddonfield which will make up a majority of the kills that we see in the movie How many kills when Michael Myers have I honestly will predict maybe zero to two Zero to two that might be disappointing to you But I think that's a realistic thing to go into this movie preparing yourself for only again considering how they continue to have Position that this movie will have a heavy focus on a character named Corey Cunningham This accusation surrounding him after a after an incident when he's babysitting this little boy Something tragic happens. He'll be he'll be a propped up as the next big bad in the town They won't let him live in peace. I'm assuming he will somehow start dating Allison I don't know how Lori Stroll will then get involved Because at the end of the day Lori Stroll from what we know all we have is that she has a memoir She's working on and going off of the trailer at some point in the movie She will end up in a battle with Michael Myers again at her house in the kitchen I will also say this there's a very good chance that the person going into the room at the top of the stairs That we saw in that first trailer that we've gotten or teaser I guess you can call it that's not the Michael Myers We think it is that's actually Corey Cunningham for whatever reason confronting Lori Stroll now. What reason would he have to kill Lori Stroll? We can only assume maybe it has something to do with Allison because I can only imagine yeah You start killing people like that bro. She's not gonna want you to be around her granddaughter So if that's why he starts targeting Lori Stroll that would explain why we see a shot of him Going into her room in her house dressed as Michael Myers But then of course something would happen with Corey Cunningham I've already done a video where him stealing Michael's mask most likely is going to lead to him Returning to Hattenfield for his mask and by pure coincidence. That's where a Lori and Michael will connect They'll have their final brawl and it might be a little underwhelming to some because of the circumstances that brought them together For that last hurrah, but for others that want to look past all of that It might be entertaining in the sense that okay It's it's still Michael Myers and Lori Stroll battling it out And if that's if that's all you care about it don't care about the who how and the why then you'll be satisfied by that But if Michael Myers literally only shows up Towards the tail end of the movie and we see glimpses of him throughout the movie But it's mostly just Corey Cunningham Using him for his own advantage a lot of people are going to be turned off by that honestly And I am going to predict that he will only have zero to two kills Zero to two kills. One of them, of course, I don't think will include Lori Stroll We know that at some point in the movie one of them will die They've made it clear during the marketing campaign so far that one of them will die Could be a twist to save us from the fact that they're going to have both of them die But also considering that Jamie Lee Curtis's words about a final shot of the movie or the final shot that she filmed Related to her sitting in a car You know, it sounds like Lori Stroll makes it out of the movie alive Michael Myers bites the dust He probably won't have too many new kills added to his added to his resume. He Besides that he's racked up a lot going off of halloween kills and what we've already seen In 2018, but I know people do come to a Michael Myers movie to see Michael Myers do some killing so Him having a low kill count might be a little bit of a problem For some people who really aren't trying to see this well thought out character driven narrative That's connected to Christine or not connected to it's been compared to Christine And it's been described as like a coming of age story too. I believe a lot of people aren't going to want to Be there for that they are going to want to see that Basic generic stuff we got from halloween kills not that halloween kills was again one of the worst things that ever came out last year But it was pretty Much a downgrade compared to what we got with halloween 2018 So Cory Cunningham don't be surprised. He makes up a lot of the kill count Uh Just so that way those of you who are listening to this if you get in that mindset when you see the movie I hope you don't get disappointed because at that point if this ends up being correct You can say that I prepared you for I'm going to prepare myself for because I literally feel as though that's what's going to happen It'll be somehow the jason goes the hell of the halloween series And I know that's a horrible comparison to make without seeing the movie But going off of how there's again saying there's a heavy focus on Cory Cunningham Michael Myers will probably be an afterthought and his kill count will only probably be Zero to two So I am going to compare to jason goes the hell because jason only appeared at the very end and the beginning of that movie But let me know what you guys think about this in the comment section below if you haven't already of course make sure you subscribe Turn on post notifications never miss a video in the description I'll have linked some social media accounts on my facebook twitter and instagram You can message me there of course limiters any movies news or reviews You like me to cover in the future and with all that in mind guys. I will see you in the next video


Hey everyone, Drew with GeoArm and I am back to talk to you about the Geo Alarm Control Panel and how to access the System Information menu in programming. Alright, what you're going to do first is hit the hamburger icon up in the top left hand corner. It's going to ask you for your master code. Go ahead and enter your master code. At this point, you want to go ahead and hit the Info tab and you will see all of the information for your system including the IMEI number, the firmware revision which I've done a video on updating that, the MAC address, the version, all kinds of different numbers in there, but that ladies and gentlemen is how easy it is to access your System Information menu within the Geo Alarm Control Panel. If you have any questions or comments, please leave them below. Take care. Have a great day. Please like and subscribe to our page for helpful programming and installation videos, product reviews, monitoring, products, and more. Visit www.geoarm.com or call<PHONE_NUMBER> that's www.geoarrm.com or<PHONE_NUMBER>, GeoArm, the worldwide leader in DIY security.


Okay, we're inside theCUBE. I'm John Furrier, the founder of SiliconANGLE.tv, and we are here to talk to Jim Blakely that leads the Intel Strategic Systems Integration, Architecture Engineering, and his team works on all the integration architecture with the vendors out there, and I'll solution architectures. Welcome to theCUBE. Thank you. I'm here with my co-host, Dave Vellante, and Intel made a good investment in the cloud a couple of years ago, past five, 10 years getting into the cloud. So tell us what's happening right now with your group and Intel. Everyone knows this is powering a lot of all the servers and desktops and other devices and embedded systems and a variety of applications out there. So tell us what's going on with you and your group. Well, so for us, the cloud really has two big components. One is with a lot of the big cloud service providers, the Facebooks and Amazons and Googles and in China, Baidu and Tencent and all the rest, and they've been driving a lot of architecture innovation in terms of how data centers come together, solutions come together, applications come together, which has been feeding a lot into what we do. It's been helping our business enormously over the last few years as those companies have grown, but maybe more importantly, they've been setting a pace for the rest of the industry that we see reflected in the private cloud as companies look to get to a much better, lower cost of compute on the enterprise, the traditional enterprise side. And so there's a lot of opportunity for those innovations that have been happening on the public side to make their way over and we're seeing that in spades right now. So on siliconangle.com and wikibond.org, we've been covering cloud, mobile and social for the past couple of years and we've been talking here on theCUBE at a lot of the events that we've been doing that the notion of systems is changing, right? With the cloud and mobile, the use cases are already driving a lot of change. I mean, Facebook's one big example of an application there. So what are you seeing right now about these new use cases? Obviously the architecture changes and big data is playing a role from using machine learning techniques to help do some configuration to a variety of other systems problems that are being solved architecturally through new technologies and methodology. So could you share with our audience out there what your view is of what's new now and has that compared to what was old and help us kind of clear that line of new and old? Sure, I mean, I think I'll give Google some kudos here, particularly Luis Baroso a couple of years ago wrote a great paper called The Warehouse Scale Computers in the Data Center and his basic premise was the data center's now the system and we're seeing that in spades and if you look at big data type problems and high performance computing more generally, cloud computing, it's all about turning the data center into a system. That's one component of it which is having sort of dramatic impacts on the data center overall. The second part of it is the whole client, distributed client environment as we've seen the takeoff in mobile devices and what's maybe not so visible to a lot of people is around embedded devices, machine to machine, internet of things, kinds of solutions that are coming to improve agriculture, to improve grid management and a whole host of industries looking at how they take advantage of the lower cost of computing at the edge and computing at the endpoint to build better applications, more satisfying applications for their end users. You know, innovation is a funny thing and Intel has a heart of innovation. I mean, you guys built Silicon Valley up and to what it is today and obviously going back to the roots of HP, kind of the founder of Silicon Valley. So you guys are no stranger to the innovation but our observation is that innovation always has kind of a, we've seen that movie before Factor but with a twist. What would you say is going on now relative to cloud mobile and social that you say, you know, that paradigm is kind of like that one but a little bit of a tweak. There's always never a one to one. You mentioned distributed computing. Obviously that's one. Things are connected. Systems is a systems operating system. So these are all concepts that have been researched, implemented maybe. What are you seeing? What's your view there from a geek standpoint of, yeah, this is a little bit of that and that kind of going on.   I'm taking that as a compliment right there.  Like there's always like, it's a little bit of network management kind of concept here but it's not pure network management but we've got some systems here.  Yeah, so you have a confluence of a lot of things happening. Now see what people talk about is real time and faster business cycles, blah, blah, blah. Simplice of the only value propositions is where the value is but underneath it is a disruptive stuff happening. What would you share? Well, so I think the opening credits of that movie usually say at the bottom and then the cost of computing dropped by 10X. And everybody's still complained. Everybody's still complained. And I think that's, take the PC initially, take client server computing, take the growth of sort of x86 and servers. A lot of it was all driven by dramatic, architectural changes that led to dramatic reduction in the cost of how things get done in an IT organization or any kind of compute infrastructure type of situation. The other big factor usually is okay, now network bandwidth changed in a dramatic way so that now what used to make sense to do one place makes sense to do someplace else. And I think what we're seeing with what's sort of triggered off this trend is first the cost reduction on the core infrastructure, compute, virtualization, performance, the cost of storage has been dropping maybe not as dramatically as some of the compute. Networking... It's going up now. Well, volume is, the volume's going up, but the cost is... The cost is going up too because the tie floods. Yeah, so the demand goes up, right? There's a lot more need for storage. That trigger point happened. The other thing that I would say has happened too is that broadband infrastructure, at least in the developed world, has gotten much, much better and that's opened up the opportunity to do things in a very, very different way than they've been done before. You were talking earlier about the cloud service providers, the Facebooks, the Amazons taking advantage of some of these trends and innovating. They are leading the way, but actually are they? I mean, will traditional IT operations follow that blueprint or do they have to take a different path because they're managing much more complexity and many more applications? Well, I think today most of the cloud service providers, the big cloud service providers have the luxury that they're, for the most part, single application sorts of houses. Now that's changing as, you know, when Google added YouTube to their portfolio that created a whole new set of workloads that they needed to deal with that they weren't before and their infrastructure had to adapt for that. But you're right, they have a less complicated set of problems to solve because they don't have quite so much fragmentation at the application level. But that doesn't mean that the lessons that they learned and that they are, you know, can share with the rest of the industry can't be applied. And you know, security is a good example here where, you know, if you own and control the whole data center top to bottom and you have great control over everything that's in there like a Google does, security is not something you're necessarily innovating a lot within their data center environment. They're innovating at the edges, certainly. And an IT organization has to solve that problem because they have those pesky users that are inside the firewall that could, you know, potentially cause problems. Jim, a question for you on the data center, obviously the area we'd love to talk about, but in the old way, obviously data center, people think of a bunch of servers, got all the gear in there. IT guys, you know, wiring the cables up and it's a data center. What's different now that's new to the data center world that they have to pay attention to? In other words, that they have to draw up journey and path to that's not a replacement, but an extension of the data center. So, let's see the data center as a classic standpoint, lock that in. It has some innovation going on. What's the new stuff that they have to pay attention to? The architects, the CIOs, that are directly related to their world. What are the key things that you see? So I think a few technology oriented trends I'll talk to. One is clearly big data, you know, is a latest buzzword, but there's a lot of new application capabilities that are coming there. New frameworks like Hadoop that'll help drive the opportunity to solve problems they couldn't solve before in terms of data analytics and business intelligence and people really should be looking at that and how they're going to architect that into the data center. The other thing that I think is gonna be relatively a profound shift is the move to non-volatile memory within the data centers as, you know, the initial phase is a solid-state drive kind of phase where now we have a new tier of storage that can add different performance characteristics than before. As that evolves and matures, you'll see other kinds of architectures for non-volatile memory that aren't storage-based architectures that'll change the way applications need to get deployed within the data center. I think the other big thing is around automation, you know, there's actually an IDC study that says the number of administrators you need scales with the number of virtual machines you have, not the number of servers you have. So every time you add a virtual machine, somebody's got to administer everything that's inside that virtual machine. So the cost of doing that need to administer it is proportional to how many new applications you create. So that's got to get solved, right? You have to be working. That's a problem. That's a problem, right, yeah. So that immediately turns around to automation, right? Go ahead. So talk about, we've got a couple of minutes left. I want to get one last question and I know Dave might want to get a few more. What can you share with the folks out there in our audience about what they might not know about your group?  And Intel's a big part of that, always has been. What can you share with them that's going on now that they might not have seen and written about or might not be aware about? That's pretty exciting. Share with them your most exciting highlights. I would say, boy, that's a tough one to narrow down. There's a lot of things going on. Well, go crazy. So we're seeing, I think a shift in sort of media in graphics, in how those get deployed in applications. Historically, graphics, media hasn't been a data center sort of thing, right? It's been either, you know, IPVBX sort of solution sitting in a telecom closet or it's been a web streaming kind of application somewhere. We're seeing much more growth in those sorts of applications. If you look at VDI, we think that's just sort of the tip of the iceberg of things that are capabilities you can apply where you do some of the processing in the data center, you use some of the processing on local devices and you can build out solutions that you weren't really able to do before because you can now deploy a solution that does some of the processing in the data center, some of the processing locally and gives an optimal experience for it. Those are, that's a, I think, something we'll see emerging over the next several years. Jim, my question, and I'd love to get your technology perspective on this is, and it relates to the whole VSpec's announcement today, on the one end of the spectrum, you've got simplicity and integration. On the other, you've got this notion of choice and flexibility. Can those two worlds ever come together? Is that possible? In other words, can you codify knowledge of patterns and workloads and things like that and actually embed it into the systems and automate it in a way that I don't have to make that trade-off in the future? Or is that just always going to be a tension that users will have to adjudicate? I think it's always going to be a tension. We're, Intel's obviously a big believer in openness and standards and the idea that systems would interoperate with each other, even a best-of-breed world. We believe that that's an important proposition for end users to be able to have some of that choice. So we would probably come down more on the open side of the house in most of this and the interoperable best-of-breed. But there's a real problem that these converged infrastructures are solving, which is the complexity of integration across platforms. It's a really hard problem. It's driving a lot of cost in the industry. And the answer won't be the same for everybody. Some people will want to have, they'll be able to buy off the shelf of converged infrastructure. Some people will stay best-of-breed. Hopefully even the best-of-breed solutions will get easier to deploy going forward. But they're solving a real problem that's out there today. Excellent. All right, Jim. Well, listen, thanks very much for coming inside theCUBE. Thank you for meeting you. And thanks for watching, everyone. We'll be right back after this short break. Keep it right there. Flash memory is a hot topic these days in both the consumer and enterprise markets. And the prevalence of flash in the consumer world on our mobile phones, tablets, and laptops is beginning to drive down the cost of the technology across all markets. Forbes magazine declared flash to be 2011's technology of the year. Another say that flash is the future of the data center. In 2008, EMC CEO, Joe Tucci, predicted that the one thing that will change the storage industry more than anything else over the next 10 years is the advent of flash technology. With EMC's recent launch of the new VF Cash hardware and software solution that leverages flash technology and intelligent caching software to dramatically increase throughput and reduce latency, that prediction appears to have come true. Today we're doing the launch of VF Cash. Everybody's really excited. Obviously a little bit of press leaks over the weekend. And people looking forward to the new product, the performance capabilities it gives. We got early morning rock bands. We got a little bit of cartoon character fun. And it's good. And I think it's, I think the marketing's innovative and I think the product's innovative. Reaction's been great. You know, press analysts and bloggers in the room which has been great, but the vast audience is really online. We've had about 3,000 people online. Just a lot of good talk about how this is a compliment to EMC technology, to VMAX, to VNX, compliment to fully automated storage steering, just taking our leadership of flash just to the next level. It's really great. Flash is gonna transform the look of the data center. In the same way as the consumer experience has been transformed by Apple utilizing flash technology, I think the enterprise experience is gonna be transformed by flash. And EMC is gonna be driving it. So VF Cash is basically taking this exciting new flash technology and moving it into service in an intelligent way. We marry that flash storage closer to the CPU complex with an intelligent software layer that determines what data should sit in the server closer to that CPU. If you think about it, flash is over a thousand times faster in terms of IOP capabilities. The performance of a persistent memory at 1,000 plus X is really a game changer for many of our customers. But now if we move it close to the server, we're able to make yet another enormous jump in terms of latency, as well as bandwidth and IOPs per gigabyte. So it really is exciting for more customers from that perspective. They get the performance with intelligence, they also get that protection that they demand in their mission critical application environments. It's a continuation of the EMC flash story that we introduced in 2008, when we first introduced flash drives in the VMAX platform, 2010 we introduced fast, we introduced VNX fast cash. This is another step in that progression. The code name for VF Cash, it was Lightning and what always follows Lightning is Thunder. So we pre-announced today Thunder, which is essentially server networked flash. So something that's shareable, something that's scalable, still on the server side, but something that can provide literally millions of IOPs to multiple server applications. That's coming next. Let's say it's EMC's commitment to innovating in the core business. We challenge ourselves, we question conventional wisdom and we're not afraid to come to market with disruptive technologies that may have some impact on that core business, but if we don't do that, then we're not gonna stay ahead.


Thanks, Jo Kannon. When I started putting together this presentation, I regretted giving this title to the organizers, because I realized that I wouldn't be able to talk about what I wanted to talk, which is showing our results. But I think it's likely to still be interesting, at least from my point of view, to try to make sense out of a very strange and difficult soup of acronyms that you might have seen this week. But if you have been attending these kind of meetings before, there are many more acronyms. And I'll try to make a bit of light on this, not easy and not obvious. Just to introduce myself, just briefly I come from the Barcelona Supercomputing Center in Barcelona. And I lead the departments with around 50 people who work on environmental forecasting. And these are the kind of things that we do atmospheric compositions, atmospheric chemistry, cloud prediction, computational earth sciences, and also services. And the two aspects, or the two groups that have been pulling some results for this presentation from are the one on services and the climate prediction one, of course. So this is one of the sentences that I took from the page that describes the workshop. And it mentions WCRP, which is the World Climate Research Program for those who don't know it. And its core projects and WCRP has four projects. Click here that works on the cryosphere, clivar, GWX that works on the hydrological cycle, and Spark that is interested in the stratosphere. And obviously, we are in clivar. This is a clivar workshop. The sentence also says that, well, WCRP has identified an interest in the K-dol climate for ability and predictability. And that brings along another acronym, DCVP. I warn you that the acronyms are going to crop up quite a lot in this presentation. So I'll try to spell them out as often as I can. So DCVP, which appears here as, well, an acronym for a concept, is also a group of people who are meeting this afternoon and tomorrow to discuss about, well, these kind of aspects. And it's one of the research foci of clivar. I'll show it a bit later. And also to continue this experimental climate prediction, which is something that is managed by another group that is called DCPP. So there is a P instead of the V. So also in WCRP, you have working groups. There are four. One of them is WGCM, the working group on coupled modeling. And this is the working group that is managing SIMIP. So SIMIP is part of WCRP. So SIMIP is not IPCC. SIMIP is WCRP. Mike will tell me off if I don't clarify this thing. So there is another working group that is relevant here, which is WGCIP. This is mainly the working group I'm involved with. And it's the working group on seasonal change to annual prediction. And for instance, WGCIP is hosting DCPP, the Decadal Climate Prediction Project, which is one of the modeling toward comparison projects of SIMIP 6. And again, it has a panel with lots of very interesting people. But WGCIP is also doing lots of other things. So for instance, one of the projects that is relevant to what has been discussed here is a project on drift and initial shock. And you've heard already a few presentations about drift and initial shock from Christoph just a few moments ago and also yesterday. So these are two of the four working groups of WCRP. WCRP also has grant challenges. And there are six here, one that I put here in white, because it's the most relevant to us. That is the grant challenge on near term climate prediction. If you want to know what the grant challenge is going to do, talk to Johanan. He has all the information. There is not much information yet, to be honest. So this gives you a fairly quick overview of what WCRP is doing. But there are other organizations inside the core projects, for instance, that are also relevant. Inside Pliver, for instance, we have a panel, which is the group on synthesis and observations, which is mainly looking into the different ocean reanalysis and reference products that we can use to validate and initialize our systems for forecasting. But there are also research full site. And one of the research focuses of a Cliver is DCVP, the Decadal Climate Variability and Predictability Group. So this is complex enough, but things are even more complex. And outside WCRP, we have WWRP, which is a World Weather Research Program. So the weather people, they get organized separately. And they are as numerous as we are. And there are some initiatives inside WRP. Some of them are coordinated with WCRP. One of them is the sub-seasonal to seasonal projects, again, concerned by some of the problems that we've been talking about here, initialization, for instance. PVP, which is a polar prediction project that has to do with, for instance, some of the things that Amy has described in her presentation, and the joint working group on focus verification research that people with this horrible acronym that you see there, they do really interesting things. I'll try to illustrate some of them. And then on this side, on the right, you have other initiatives and panels that are very, well, you might be familiar with, which is the IPCC, that takes a lot of information from SIMEP, but also from the rest of WCRP, at least for working groups one and two, and also GFCS, which is a global framework for climate services, which is, again, something, another initiative inside WMO. And WMO is outside all this and is much, much bigger. So there are many more panels and committees. And I'm sure that you will agree that it's quite difficult to make sense out of this. And I guess that people are already quite happy of being here. And, well, that's already organizing your work and your contacts enough. But let me be a bit controversial here. In my opinion, this is very compartmental. It's preventing progress. And I think we need to be a bit more promiscuous and try to be engaged as much as we can in activities that other communities are doing. Because somehow, this is a clever meeting where, for instance, the Spark people are not present or we're not people working on the grand challenge on cloud circulation and climate sensitivity are not here. But climate sensitivity is fundamental for the kind of thing that we are doing. Why this happens? Well, I guess that we all have ideas about why this is the case. So let me go a bit more into some of these initiatives and panels. Simip V was designed to feed the fifth assessment report of the IPCC. And at least for the decadal prediction parts that contributed to Chapter 11 of the Working Group 1 report, there were a few lessons. This is my selection. I'm sure that many of you will remove some of them and add many, many more that I'm sure that will be very important and very, very interesting. But just to start with, there is a climate prediction and a climate modeling community that got together to start the decadal prediction. And the first thing that we realized after two years of fights is that we were talking about different things with the same words. So there was a massive language problem. And I was talking about this with Susanna yesterday. It's a horrible, terrible situation. And something else that it was obvious is that the decadal prediction has skill. The skill, the skill over the continents, at least for temperature. For precipitation, it's not the case, except in certain regions. But there is skill. The decadal prediction has skill. Sorry if I repeat it, but many people say that the decadal prediction doesn't have skill. It's not true. There is skill. But most of the skill in temperature, at least in temperature, is forced by forcings, natural and external forcings. And we saw in Gavin's presentation, you talked on Monday, Gavin, I think it was, that if we had the right forcings, we could have predicted the, well, much better the slowdown of global mean temperature. Unfortunately, we don't have the forcings because we are doing prediction. So we have to live with that. We also need to have many more handcuffs than the ones that were done for CIMIP-5, starting every five years. Volvo yesterday showed how the skill scores go up and down. And we've seen other examples as well. And also, it's very important to use the same model configuration as the ones that are used for climate experiments. Christophe has been talking about the pacemaker and the fixed anomaly experiments. And he could use the same model that he was using for the historical experiments. So is it the same version that he used for the decadal predictions? And this is the way we can really learn about our decadal prediction systems. Because if we start changing the versions, it's going to be very difficult to make progress. So learn about your model and its drawbacks. And the other really tough thing was that drift, initial shock, and systematic error are hampering the progress of decadal prediction. And several of us have been talking here about why we didn't do more flux correction for decadal prediction. Well, I don't know. Maybe it's a time to do some work. Julia has been doing some. And E.C.M. Doliev as well. But it's not systematic. So this is one of the plots that was showed before. These are the decadal predictions in solid. Four from SIMIT 5, from systems that started every year. In gray, you have the observations in dashed. You have the historical runs for the left global mean temperature on the right AMV. It's a simple AMV index, which takes an aerial average of SSD over the North Atlantic minus the global mean temperature. And it's quite nice to see that the AMV is better predicted when you initialize than when you use the historical runs. And it's also striking the similarity between the global mean temperature predicted and also from the historical runs. But we have this. I think it was Susanna who mentioned that the historical runs were overdoing the global mean temperature in the last 10 years, while the initialized simulations didn't. And there might be two ways to explain what happened here. One is that the SIMIT 5 systems were very good at facing in the internal variability. The other one is that they are correcting the incorrect forced model response in our systems. And I would like to advocate for the second one. So it's true that SIMIT 5 didn't use the correct volcanic aerosol forcing, and the aerosols were not correct. But somehow, by initializing, we are correcting for these aeros in the forcing. So basically, there are two things that we should expect from the KDL prediction. One is the phase in of the internal variability, which is something that we've been talking about here, which is something really tough. But the other one is the correction of the forced model response up to a certain time into the future. And let me go a bit more in detail with this idea. But this is another sentence from the page describing the workshop that says that, well, the warming hiatus caught us by surprise. But there was a failure of the initialized coupled model predictions to detect it. In my opinion, this is not true. They detected it. They predicted it in hindsight. But was it for the right reasons or not? It depends on what the right reasons are for us. But for the users, the right reasons is whether they can really make better decisions based on the information that we give them. So more on the correction of the forced model response. These are results from a very nice experiment that was run years ago at the Met Office. A huge data set was constructed using the perturbed parameters, the perturbed parameter approach for the perturbation of the model, the several configuration of the model were run in the KDL focus mode with initialization and without initialization. And one of the interesting things about this system is that you have several configurations of the same model with different responses to the forcings. So these are, well, measures of scale in terms of correlation for temperature. And the vertical axis represents the aggregation of the full costs, especially while the horizontal axis represents the focus time. So we are aggregating in focus time or we are aggregating in spatially. So we are smoothing the maps, basically, as we go up in this axis here. So the first column corresponds to the uninitialized runs. The second one to the initialized runs. And this is the difference in correlation. And the difference between these three configurations, but in average, well, the configuration one, two, and three, is that this one, the one at the top, has the highest slope in global mean temperature in the historical run. The second and the third are the ones with the smallest slopes in the global mean temperature. So what we can see is that the impacts of the initialization is much bigger when we have shallower slopes in the global mean temperature than stronger slopes. The interpretation of this is that, again, the initialization is doing more than just phasing in the internal variability. And the reason why we are benefiting is because the models have the wrong force response to the 4.6. Starting every year, the hindgust, it makes the hindgusts much more expensive. It's not the same doing hindgusts every five years than running hindgusts every year. But it has an impact on what we can say about our systems. This is the skill for the AMV, it's a function of the focus time. And each color corresponds to a different time averaging of the forecasts. So in red is the root mean square error for the forecasts taken every year. And well, in green is the result for the forecasts when we have averaged them every three years into the forecast and so on. So we have more and more smooth version of the time series. So when we initialized every five years, which is what we see here, we see apps and downs. And we saw some of the results yesterday that, well, they seem to suggest that there was a recovery skill after a few years into the focus, which sometimes is a bit difficult to explain. However, if you take exactly the same period, so 1960 to 2005 for the verification, and look at what is the root mean square error when you initialize every year, basically we have exactly the same information here, we are not increasing the number of degrees of freedom of the skill. But we're having much smoother versions of the skill measures. And the reason is that we need to sample the internal variability during the period when we are making the forecasts. I'm not saying that it's absolutely necessary to do high gusts every year, but every five years is definitely too little. And, well, this is something similar when looking at individual models. So I'm talking all the time about prediction. And in prediction, we are initializing a climate model. And we do this, as I said before, to address internal variability, to take into account the incorrect force model response. And we initialize the model by using the best available observations. So we have to link to the observation lists, whoever they are. That's already a first challenge to identify them. To transfer that information into the couple model, avoiding imbalances, again, it's easier said than done, as you heard the discussion yesterday. And then we need to run with initial perturbations that, well, produce a spread that is representative of the uncertainties that we have, which are a lot. But unfortunately, the uncertainties are uncertain. So what we do is something like this. We run a set of high gusts with an ensemble, in this case, well, let's say five members for the second illustration. One year later, we do the same thing. And then five years later, we've done already six high gusts. And we carry on doing this thing every year. And we start from an observational data set, some measure of what the current state of the climate system is, not necessarily the best estimate of the state of the climate system for prediction. The reality is slightly different, because Susanna, I think, showed this, or Daniela showed this plot from the IPCC, where we can see that in the IPCC, we had models that used the anomalous initialization approach, the full field initialization approach. But even if they used the anomalous initialization approach, the systematic errors were massive. And those using the full field initialization approach, they're suffering from these drifts that you see here. You don't see the initial shocks, but they are astonishing. And it's really enlightening to have a look at the KDL forecast and see an animation of any variable using six-hourly or three-hourly values. With a month is enough. You don't need to go any farther. The model is all over the place. It's incredible. It destroys everything that you are putting in. It's so amazing. And it's very nice to see, because you see all the processes that we've been talking about here in action all at the same time, without interaction, with interaction. And the problem is that we are forecasting while the model is doing this. So we are trying to say something about the future while the model is in complete, well, going through a complete reorganization of the information in itself. So we need to do something. And some of the things that were done was a discussion about an evaluation of the relative merits of the anomaly initialization approach. And there are many approximations that have been implemented for the anomaly initialization approach. So several institutions carried out, again, as a result of SIMIT 5, but also as part of SIMIT 5. They carried out intercomparison experiments where they either initialized in the real world, which is here in green. And if you do this, then the model just goes to its own attractor, so it drifts. Or you initialize here, and you initialize in the model world, which is already biased. But at least you know that the initial shock is going to be smaller or inexistent. So there are many examples of this. I'm just giving one example here, but the Metaphys looked into it, and MPI and the University of Hamburg have looked into it. And many other people, for instance, Environment Canada has done the same thing. But unfortunately, until now, there are very few proofs or very few hints that anomaly initialization is giving us a bit of skill. In fact, it seems that full field initialization is giving better skill in spite of the initial shocks and the drift. And this is an example from Danila Volpi, who looked at different ways of implementing anomaly initialization in the ocean, taking into account just the correcting the anomalies in TNS or in T and density, or introducing some anomaly initialization as well in the CIs.  For instance, in the AMV or in the Arctic CIs volume, but after three years, no distinction, no difference whatsoever in terms of skill. And you have to really fight hard to get the anomaly initialization working, because you have all sorts of drifts and misplacements of the observed anomalies into the model. Basically, it's a data simulation problem in not in the observed attractor, but in the model attractor, far from Danila. So it's a really nice challenge, but we have to do something in the meantime to provide the users with some information. We also did some additional work. I won't go into the details. To try to understand a bit more why, in underserting conditions, we were having a better skill in the first one or two years with the full field initialization and why in other conditions we were having it with anomaly initialization. And we used a hierarchy of models, basically the Lorentz model, the Peñan-Kalne model, which is a version of the Lorentz model with nine variables, and also the Van Itzeman, the Cruz model, which is a simplified couple model with less than, well, it's 29 variables. And what we found out using a very simple mapping approach to implement the anomaly initialization is that when the attractors are incompatible, when the model attractor and the observed attractor are not compatible, then there is a massive initial shock. The initial information is lost after just a few time steps. And then the model is better in using an anomaly initialization approach. But most of the times, the attractors are not fully incompatible. They are compatible, but they have differences, for instance, in certain variables in the kurtosis or in the skewness. And that's making the linear approach for the implementation of the anomaly initialization, which is the one that has been implemented until now, really unfeasible in giving the, well, unsatisfactory results. These are two of the references of this work using simplified models. But it's an example of how we need a hierarchy of model that goes even beyond just looking at Amy runs or ocean only runs. We really need to put under test the approaches and the concepts that we are using. So there is going to be a bit more discussion about this next year. We're organizing a workshop in Barcelona. It's sponsored by the specs and the preface European projects. And it will also receive sponsoring from WGSEP, which is the working group on seasonal to inter-annual prediction. And basically the idea is to come up to an agreement in terms of the kind of experiments that we need to address this issue. The issue of the initial shock and the drift. But at the same time, discuss about the feasibility of coupled initialization. Note that I'm not talking here about coupled data simulation. I'm talking about coupled initialization, which is a different thing. To address the problem. And for this, we need representatives of the global, of the group on synthesis and observations panel from of Glybar. So again, trying to be a bit more promiscuous here. And the other objective of this workshop is to discuss about an eventual recommendation from WGSEP on the problem of bias adjustment. So in the end, the users, they need something that is not an anomaly. They need a field that they can use. And a field that resembles the reality. And this is something that still requires a lot of work. Symmep six, this is coming up. Christoph has already mentioned some of the activities. Of at least the decadal component of Symmep six, DCPP. In that context, decadal prediction can benefit from a better understanding, from the possibility of looking into the control runs and understanding the characteristics of the variability in our models. And also from the infrastructure. If we want to share data, it's all done. So it's already there. We'd rather be close to what WGCM is already doing. Other MIPs, other modeling into comparison projects can also benefit from the decadal prediction part of Symmep six, because there will be more people looking at the reduction of the systematic error. We have a massive interest in reducing this because it's destroying our initial condition information. Not all, but a lot of it. And also because we'll do a continuous verification of the models. We are continuously comparing with observations, with real observations. But it's very expensive. These are really huge experiments. At the same time, there will be a real-time decadal prediction component in Symmep six that is led by DCPPs, the one called component B. And there will be also hopefully a lot of work on looking at other processes that might bring additional skill. So before Symmep six, we have to work on issues like for instance, the impact of the volcanic aerosol and how we project the volcanic aerosol into the future. It's one of the tasks of component C. Because in the end, we have to recognize that we can't predict volcanoes.  We also need to set benchmarks. It's very good to keep working with the couple models. They are very complex. We've seen lots of their limitations. But at the same time, there are people working on very interesting alternatives that can really help the development of the model, but at the same time, be used as a benchmark. And a good example is you could see it in an image-suckling poster this week. This is an example of the skill score in terms of correlation for the critical decadal prediction system that they've built. This system has the additional interest that it's a system that is calibrated. So it's probabilistic. It's not just giving you a value for the next five or 10 years. It's giving you a range of values that are calibrated with the observations. And then we have observational uncertainty. The observational uncertainty is hurting us in creating the initial conditions. And it was very nice to hear the argument yesterday between Dirk and Alicia. Not that I like to hear you arguing, but I like what you say when you argue. But they also limit our ability to know what the skill of our system is. And this is an illustration from seasonal forecasting. It's from a set of seasonal forecasts. We run with East Earth, the new version of East Earth. And this is the skill for Neo 3.4. For Hankus, started on the 1st of May and running for four months. And it's a skill for each month for the same system but evaluated against four different observational data sets. The one from ESA, it's a 25 kilometer data set that has been recently released. All the others are low resolution data sets. And we found out that when validating forecasts from high resolution systems, well, you have to take into account also the uncertainty that is associated to the resolution of the observations, which is again something quite fascinating to look at. Sorry, GFCS, again, another component. This one is outside WCRP. So you don't need to feel concerned about it, but it's a very nice thing. It stands for Global Framework for Climate Services. And basically we are here in the research, modeling and prediction part.  And the observationalists are a bit here, next to us, but the bulk of the climate services is a completely different kind of problem, where we have something to say and where some communities are waiting for us to interact with them. These communities, they get together and one of them is getting together in the Euphoria project, which is another European project. This one led by the MetaFest. And they came up with these principles for a successful climate service. So it's actually quite nice. It looks funny, but it has a lot of depth into it. So it's a matter of defining your problem, having a roadmap, being flexible, verify and evaluate and monitor, being transparent to avoid tensions, being able to listen and use the principles of research and development. These principles that you might think that they are very obvious and that they've been put there for the users, actually they are not for the users. They put them together for us. So this is what they expect from us to have a successful climate service. And on top of this, it's actually interesting to see what they are coming up with. They're the right, really interesting reports. There is a very nice one that was recently released. It's the ethical framework for climate services. I know it sounds a bit pompous, but it's really interesting reading, very short. And it's based on four core elements that they want to propose that everyone interested in climate services abide, well, follows. Integrity, transparency, humility and collaboration. And as obvious as they are and as much as we think that we follow them, I think I did my own exercise of, well, critical exercise to try to say if I was following them and I failed in two of them. So I'd suggest or I would like to encourage you to try something similar. In any case, something, a mistake that we are doing, coming from the WCRP side, is to think that climate data is climate information. And it's not true. Climate data is just a piece of the climate information puzzle. So something very nice about climate services is one initiative that is taking place within the working group on seasonal to intranet prediction and in particular, as part of the Decadal Climate Prediction Panel, which is the multimodal Decadal focus exchange. If you go to this website that the Met Office, you'll find plenty of information and it will be kept under development by DERC, DERC's group in the next few years. We also have, especially from our community, downstream services. So portals that distribute data and make very nice plots and they provide some information. Well, they have a link, for instance, to the ESGF data. For instance, now we have Decadal data on Decadal predictions on ESGF. And it's a key aspect for success. It's good to have this thing to engage with climate services, with the users. But there is a bot. And the problem can be illustrated with this plot from Andrea Taylor, who is at the University of Leeds. So this is a very interesting plot. Andrea is a psychologist. So, and she writes very nice papers that I really recommend you to have a look at. She works on seasonal prediction. And she, as part of Euphoria's conducted a set of interviews, a few hundred. And among those interviews, she analyzed the users of seasonal forecasts that responded to a set of questions. And that these are the forecasts that indicated in which way they received information about uncertainty. So here we have uncertainty in the form of range of values. So an interval, which is something that we do very often in certain, sorry, range of values or confidence intervals or variable descriptions of likelihood. Very tough to communicate. Role data, well, that's something that they already say that they received. But what they didn't receive is information about how good those forecasts are or indicators of the signal strength and what it means or information about possible sources of error. And in portals like this, we can't do this because that goes down to human interaction. And unfortunately, human interaction takes time and engagement from people. On top of this, we are providing information that this information needs to have a quality measure. And one of the groups inside WMode that is really doing a lot of work in this direction is the joint working group on focus verification research. And the address questions like how do we interpret something like this contingency table that we have here. So this is a table where we put the, well, let's say we have a number of events that we've been looking into and we have also forecasts for those events. And usually we look at this column here. We look at the events that were observed, I don't know, the hiatus, for instance, or the shift in the IPO. And we look at whether the model was successful or missed it in the focus. And we also look a lot at this one. So there are plenty of presentations at the EGU or the AGU saying, well, I focus this thing. But it was so good that I got the shift at the right time. It's really good. But what we never see is that box. That's a box in which you have all those cases that the model reproduced or predicted an event that actually didn't happen. And this is the one that is hurting us. What is hurting us is not this one, but this one because it's the one that is affecting the users. The users are affected by cases in which we cry wolf and the wolf doesn't come. This is the, that's a case. And let me just wrap up and conclude with an example for instance, a user of decadal predictions. And this is the case that we built in collaboration with a reinsurance company. And there are other examples along the same line in which we demonstrated to the company that the advantage of the decadal predictions with respect to the historical simulations for the next 10 years, for their trade, for in particular for the prediction of the impact of tropical cyclones in the North Atlantic, is that the decadal predictions are not just more skillful because we can predict the AMV, but they are also more credible because we have a better estimate of the actual uncertainty of these forecasts. Because we have reduced in a certain way, in a meaningful way, the uncertainty associated to the initial conditions. And I'll jump that one and go to just last overhead in which I would like to make a plea for all of you to get engaged into activities like this one, this is a fact sheet series that we started as part of this specs project in which we try to explain with very simple words and using a vocabulary that has been said previously what we are doing, the kind of problems that we are dealing with. And I know that Ed is doing plenty of things along these lines, both inside the community and outside the community as part of his gliver activities. There is a pressing need for things like this because otherwise we might be out of business sooner than you expect, unfortunately. And this is something that, well, we might see before we retire. Just a summary, there is a complex ecosystem of international activities and many of them are relevant to decadal prediction and predictability, but we need to know who they are to take advantage of what they are doing. And at this stage, there is a lot of information missing about this. There is a broadening range of users who is asking for information and giving them information about the decadal predictions even if the decadal predictions don't have skill is a fair thing to do because it will be a matter of providing them with a good climatological focus which is, again, not as trivial as we might think. Decadal prediction is showing signs of providing useful information. We've seen examples here. Emerging all this information into a reliable source of information is not a trivial task either. The users don't want to have five different focus systems and then find a way of what to do with it. It doesn't work this way, unfortunately. The models have drift and there is a massive initial shock in a full field initialization. The initial shock doesn't go away even when you go into anomaly initialization and it's something that we have to understand a bit better and basically we need to keep asking for investment in observational networks. We've seen already how important the lack of observations is. Increased collaboration and a reduction of all aspects that are at the origin of model error. And I think I'll leave it here. Thank you.