Well, thank you Keith for that introduction and thank you for inviting me and also thanks to all the participants for giving up their Christmas preparation time to do so. So as the title suggests what I'm going to do in this talk is explain the fundamental limitations of what we can learn from data alone, even using the fanciest machine learning techniques. And when I say that causal reasoning and knowledge comes to the rescue what I mean ultimately is that Bayesian reasoning and causal Bayesian network networks come to the rescue so I'm going to explain what these are. And I'm going to use them to resolve two statistical paradoxes which are called Simpson and Berkson paradoxes, which inevitably compromise what you can learn from data alone. Can I just check that everyone can hear me okay. We can hear you. There's no slides yet I guess that's. No, no, sorry that that should. Let me just check why that is. Okay, now it's coming. Okay, so that's the thought I was sharing and I wasn't okay. And so I'll present some real world examples and their Bayesian network solutions. Now, in case there's a suspicion that we can overcome the kinds of problems that I'm going to talk about if we had really big data. Then I'm going to end with a surprisingly simple example which slams the door on that idea. So I'm not saying that machine learning algorithms are waste of time. There are many important applications like certain types of classification and image recognition, whereas, whereas possible with sufficient data to achieve human level accuracy but it's not AI. And those techniques don't help for most types of critical decision making where data are limited so I'm talking about problems like assessing and avoiding cyber security risk, determine an optical medical treatment for specific conditions. Determining whether a new type of aircraft will meet aviation safety standards, predicting operational risk exposure of a bank. These are some of the many areas where these Bayesian network methods, which enable us to fuse limited data with causal knowledge have been used successfully. And this will also be of special interest, presumably to many of you is that there's actually this is I found this recent special journal issue which focuses entirely entirely on the use of Bayesian networks to improve improving environmental risk assessment, which is also something that we've worked with including done some work with Keith himself and their group. So, here is a typical example of a big data driven problem where machine learning algorithms are used. So banks gather comprehensive data on customers to whom they give loans, and they use this information to help them future assess to help them future assess risk of future customers are those most likely to default on a loan. So, here I've highlighted. So here I've highlighted the customers who've defaulted. Now what the statistical machine learning algorithms do is learn what the distinguishing features are of those customers who default and how they differ from those who don't default. The first problem with this type of data so it might be big. It's completely missing data for entire class of customers who we also need to learn about. Specifically, yeah, sorry. So I'm having trouble with my screen. So specifically, it's restricted to customers who are given loans. And it tells us it's got tells us nothing is there's no data there for an entire class of customers for the for those customers who weren't given loans. So we say that such data is censored and therefore biased. And we can't learn anything about customers who refuse loans. And this creates this creates other problems leading to fundamentally flawed algorithms. So take a look at the ones are now highlighting. So these are this is a handful of customers who happen to be unemployed teenagers. And they've been given very large loans and they don't default. See there's no default here. Now we know that most unemployed teenagers are going to ask for big loans are refused because they'd almost certainly default. But that those teenagers never made it into the data and so we don't learn such obvious characteristics. Instead, what we have here is a small number who don't default because they were special case exceptions. It turns out they were the children of wealthy customers known to the bank. So instead of learning that unemployed teenagers are high risk to purely data driven algorithm learns the exact opposite unemployed teenagers apparently don't default. Now here's a model which represents essentially what all machine learning and statistical algorithms are doing. There's they basically learn this this the target outcome as a function of the inputs for which they have data. But the model can't learn the impact and relationships involving unobserved variables like I've highlighted here. So the variable loan given was censored because we only had data for for those who were given loans. And we're also missing special client information and latent variable loan suitability. So expert domain knowledge and common sense understanding of causality can provide the graphical causal model before considering the data. And that also would include relationships between things like salary being dependent on agent employment. Now there are real benefits in simply drawing graphical causal models like this before doing any data collection analytics. We need to guide the collection of the right data and avoid most of the errors that people make when analyzing and learning from data. But to use such models for prediction inference and decision making. We need a computational framework to support it and base there and provides exactly that. So to keep things topical I'll introduce base room by way of the coven 19 testing example. So let's suppose that the false positive rate for a particular test is 2%. So that means that for every 100 people without COVID, there'll be two test positive and 98 will correctly test negative. Suppose there's 20% false negative rate. So that means that for every 100 people with COVID, there'll be 20 or wrongly test negative but 18 will correctly test positive. So let's suppose that the current population infection rate is one in 200. And that in random testing Sarah test positive. So what's the probability that she has the virus. Well, let's imagine a random sample of 1000 people because there's a one in 200 infection rate about five of these are going to have the virus. But that means that 9995 of the others don't have the virus. Now, because there's this 80% true positive rate for the test that means about four of the five who have the virus will test positive. But because of the false because of the false positive rate, which was 2%, about 20 of the other 995 will test positive. So if we actually strip away all those who don't test positive are only less left with those who test positive here. What we have is about 24 testing positive of whom the red ones which is about four actually have the virus. So that's about one in six, which is less than 17% of those who test positive actually have the virus. So Sarah test positive what we can actually conclude is that it's about one in six probability that she has the virus. Now what you had here was basically a visual explanation of Bayes theorem. But formally we can think of Bayes theorem as providing the underlying computational framework for performing the inference that we need in causal models. So with Bayes we start with an unknown hypothesis H, such as in this case the person has covered. We have a prior belief in H which we express as a probability in this case one in 200 or 0.005 and we've got a corresponding probability table for that. Then we get some evidence about H in this case a test result and note the direction here is a causal direction. The test result is caused by the virus status and not the other way around. But we know the probability of the evidence given whether H is true or false and we write this as the probability of E given H and the probability of E not given H. And we've got the probabilities for that, which can also express this rest associated with a probability table associated with this node. But what we really want to know is the revised probability of H given the evidence I the probability of H given E. And this is called the posterior belief in H I we want to know how to update our belief in an unknown hypothesis when we observe evidence about it. I we want to be able to infer back from the high evidence to that unknown hypothesis and base there and provides the answer to this with this formula. And that expands to this and all of these terms, we actually know the values and when we plug those in, we actually get that result which was approximated by the visual representation, which is just under 17%. Now, the good news is you never have to worry about doing any of these calculations manually, because what we have here is actually a very, very simple example of a Bayesian network which is just a directed graph like this with these associated probability tables alongside each node. And in these models we can do the prediction forward from inference course effects and backwards. And here's an example of that that model actually running in one of these tools I'm just going to run this now. So first of all you can see I can select that node and look at the node probability table. There you see it. Now I can enter an observation like positive on the test result here, and you can see update automatically updates when I run the model, the probability here and if I put negative in and run the model again. You can see it's updated to very low probability not point 1% the person has covered. Now, of course that was a very simple model most models. Most problems like this when you dig deeper and more complex and involve multiple unknown hypothesis and evidence with complex interdependencies so we might for example, have a second run a second test. So that a person having covered is dependent on whether they've been in contact with an infected person, and whether they've got symptoms or not of course is going to increase that that probably is going to increase if they've got covered.  Well actually let's run that model and let's see what happens with that model when we run it in at all. So you can start off by entering a positive observation here, which when we run it updates the probability of COVID as well as the infection rate, we enter some information like no symptoms, and we're over over 60 Ruan the model is decreased the probability is decreased. If we enter a second pot net test which is negative you can see it goes down even further. So we had a positive test, things change completely. So that gives you an example of a model running. And actually, here's a real model which our research group developed from a combination of data and knowledge working with clinicians. Models like this can be deployed and run now on web or mobile phone with a simple questionnaire interface to shield the users from any underlying complexity, and you can actually go to this website and run the model which I'll just sort of quickly demonstrate. So all that you need to do is you answer relevant questions and, you know, you click a calculation button so here we might enter the result of a test if you've had one you don't need to have had a test. You run the calculate button, and you can see the result calculated against the baseline and you can provide information about underlying medical conditions. You can enter information on any other symptoms, etc. Now we've got a rigorous and practical framework for causal models that enables us to combine data and knowledge. So we're going to return to the problem of why this framework is needed. It's the fundamental limitations of what we can learn from data alone. And the starkest and best known examples of the danger of reading too much in what can be learned from data as furious correlations like this one. For example, very close correlation between number of people drowned by falling into a pool with number of films Nicholas Cage has appeared in Japanese passenger cars sold in the US correlates very closely with suicides by crashing of vehicles. These are genuinely spurious correlations without any causation. But this one looks like it's the same it looks kind of like weird per capita consumption mozzarella cheese and civil engineering doctorates. But actually it's more problematic. Because it isn't spurious. The problem here is that there is there is actually some underlying if this is actually predictable, because there's some underlying as an underlying hidden common cause explanation. Namely wealth, as the wealth of a country increases over the years so does its consumption of many things whether it's mozzarella cheese or civil engineering document documents. Once you've got this causal model you can also see the explanation for the association between eating engineering doctors. And basically we can drop that link because the link the associate is just an association because of that hidden cause explanation. But the problem is that in general, there may well be a direct causal relationship between variables which also have a common cause. For example, we are interested in the effect of a specific treatment on the outcome of person with a particular medical condition. We know that there's a direct link but we don't know how much it is and the fundamental problem with trying to learn such a relationship from data is the hidden cause where explanation often means that we learn the wrong thing. And this is where I focus on the two paradoxes which are at the heart of all such problems here. The existence of a competitive a confounder can completely invert the correct relationship that we might relearn we might learn between treatment and outcome. So that's the base and basis of Simpsons paradox. But they're also maybe a hidden collider. And this can also completely invert the correct relationship. There are conclusions that you come to based on the data alone. So typically the collider will be a constraint on the data sample, which may have inbuilt bias. And that's just what we saw in the bank example where the collider was the fact that the data was constrained to a sample of people who previously been given bank loans. So this is called Berksons paradox or collider paradox. So this is a real example of Simpsons paradox. What we have here is a large observational study of patients treated for kidney stones. So the study was based on equal number of patients given two particular treatments and be there was a successful outcome for 78% of the patients treated with treatment a successful outcome for 83% of the treatment of the patients treated with treatment B. So clearly treatment B was most effective. Well no, it turns out that treatment A was more successful when you looked at each different type of kidney stone that was that was treated. How is this possible. Well again, what I'm going to do here is actually is actually run the model to show you what's going on. So, first, first of all, we'll confirm the results I just showed you the success rate for a is 78% and the success rate for BS 83%. But what I'm now going to do is reveal the hidden confounding variable.  See for stone be it was much more likely that it was a large treatment be a much more likely large stones were treated treatment a. It was much more likely there was a small stone. Now, what we're going to do here is run the model but fix it for the size of the stone fix it to a large stone. And we're going to run it now for a and the result is 93% success for treatment a for be with a small stone. The success rate is 87%. We're now going to fix for fix for large stones. We're going to run the model for treatment be that 69% success rate for treatment a. It's a 73% success rate. So what's actually happened here is that for both large and small stones, I was more effective. But the overall results are confounded because treatment a was much more likely to be given to patients with large stones, which are more challenge which are more challenging to treat successfully. So although the study had an equal number of patients treated with both a and b, there wasn't an equal number of patients with large and small stones treated by each treatment. In other words, the study didn't control for the confounding variable stone size. The critical benefit of a causal Bayesian network model is that we can use it to simulate a proper randomized control trial, just from the observational data, I we can simulate the control we can simulate control of the confounding variable. All we have to do is basically cut the link from the confounding variable to the stone size I the thing that we're going to intervene on. And this enables us to calculate the overall unbiased result. So let's do exactly that. Again, in the tall, all I'm going to do now is make a copy of the model. So I'm just taking that model. I'm doing a copy and pasting it. It's exactly the same model. It's retaining all the information, but I'm breaking that link now. So what I get now when I run the model is the overall unbiased result. So overall 81% a success. But when I look at now the unbiased treatment of a the overall success rate is 83% and for. And for be the overall success rate is 78%. And so the overall treatment success rate 81% for treatment 83% treatment be 79% and there you see was that that reversal. Now here's another interesting real data from Cambridge University admissions. It's another nice example of Simpson's paradox. So it was argued that the admissions data proved bias against female applicants because the overall success rate among women was lower than that of men 23% to 24%. But in every single subject. The acceptance rate for women was actually higher or at least equal. Now what's happening here is that there are some subjects like engineering, which have an overall higher acceptance rate than other exception than other subjects like veterinary medicine, more women apply for these things than those. And that's what explains Simpson's paradox. So specifically, in this case, gender is a confounding variable causing Simpson's paradox. Let's move on to Bergs's paradox, but use exactly the same kidney stone example as before. But again, the wrong conclusion arises here for a different reason and that's the collider bias of Simpson of Bergs's paradox. So again, we've got the an equal number of patients treated with A and B. For this data, the overall success rate for treatment A has gone up it's 89%. And for B, the overall success rate has gone up to 95%. So we already know that now we already know that because those have gone up there's probably some kind of sample bias here. But also we already know that treatment A was more successful overall when you narrowed it down to the different types of stones and we know that if we controlled for that, A was also more successful overall. So we know that these results must be wrong. But the explanation is different in this case. So remember, those are the true unbiased results that they should have been. A was 83% overall successful, B was 78%. So again, what's the explanation in this case? Well, in this case, again, I'm just going to enter those observations to prove that we got the site, this is what it gets, that 89% for A and for B, we get the 95%. But what's the explanation? I'm going to reveal the hidden node here, which is the confounder, and you can see that is set to true. So we're restricted to what was in the sample. And you can see that more A were in the sample than B. If I remove the restriction to the sample, so I'm going to remove that, we've now got the unbiased results. I'll run this now for A and we'll get the results of 83%, which we know actually is the correct result. We've seen that from the previous study. For B, the overall unbiased result is 78%. So as soon as we remove the constraint on the sample, we get the correct results. Now, interestingly, the, yeah, sorry, I'll just, yeah, sorry. So interestingly, both of those paradoxes are known to have been the cause of incorrect conclusions about risk factors for COVID, based on very large observational studies. So for example, different studies concluded strangely that smoking reduces COVID risk. So they concluded that there was this causal link, that that may have been a flawed conclusion. So crucially, these studies were biased because they restricted to people who've been formally tested. And at the time, when these, when that data was collected, almost the only people were formally tested with those who either had severe COVID symptoms or were healthcare workers. And because healthcare workers are less likely to be smokers, yeah, because healthcare workers are less likely to be smokers, the sample was biased because it meant smokers were underrepresented in this sample. And healthcare workers were also more likely to get COVID, which means that we also had this healthcare worker as a confounding variable. Now once you recognize these colliders confounders, the direct causal relationship from smoker to COVID actually vanishes. And moreover, I predicted that using this model that you would get a similar incorrect conclusion if you looked at a factor like stress rather than smoker. It would also conclude that stress reduces the risk of severe COVID symptoms. And I wrote this up in this paper and lo and behold, while I was writing the paper, studies came out with exactly that result claiming that the hypertension stress also increased the risk of suffering severe COVID. That was all predictable and it's completely wrong. Now, the fundamental limitations of purely big data driven approaches has been brought to the fore recently by world leading computer scientists and AI researcher Judea Pearl. He has argued convincingly the need to incorporate causal knowledge in order to improve the state of the art of AI learning. And he characterized this by a his so called three step ladder of causation, where the steps were seeing doing and imagining. Now level one of this ladder this ladder we learn by seeing, but we only learn associations this way such as from this data is this particular kidney stone treatment effective, or in our bank loan example. We can learn whether unemployed people given loans are more likely to default. At level two we learn by doing interventions so we learn about things such as if I have this if I have this particular kidney stones treatment will it be effective for me, or in the bank example if I'm unemployed will I default on a loan. At level three we learn by imagining. Now that way we learn about counterfactual such as if I hadn't had this particular kidney stones treatment. Would I still have been okay, or in the bank example if I've been if I've been employed would I still have defaulted on my loan. Now Pearl argues that if we rely on statistical machine learning from data only, then we can only ever get to level one on this ladder. Getting to get into both levels two and three requires causal knowledge of the relationships, not just between factors in the observable data sets, but also including unobservable ones. Now I already showed you how the Bayesian net approach enables us to get to run two of the ladder. We accurately simulate the effect of intervention by breaking any links that go into the variable want to intervene on. In our example, that was the, the confounder treatment type. Sorry, that was the, yeah, that was the treatment type. So we intervene on that. But Bayesian nets also enable us to get to level three on the ladder. By creating a twin network version in which we model both the real world and the counterfactual world. But again, we drop the link into the treatment variable into the thing we want to intervene on in the counterfactual world. So suppose we learn in the real world that I had treatment be and it was successful, then what it's done is update my belief in the unknown probability of the stone size. So it's much more likely that the stone was small. And now, in the counterfactual world, I can simulate the intervention. What would have happened if I had treatment. I and it turns out it would have been it's very highly likely that it would have been a success. So causal models really done come to the rescue where machine learning from data alone files. Finally, as promised, I'm going to show you why even the biggest possible data might not be enough to learn the things that you want to learn. So I want to imagine that we've got trillions of data on the relationship between just two binary variables and independent variable X and a variable Y, which we believe depends on X. Imagine that we've got trillions of these pairs of numbers showing the relationship between X and Y. Then surely we must be able to learn the relationship between X and Y. You think so. The problem is that there's zero correlation between X and Y. I went X is zero Y is equally likely to be zero one. So the best machine learning algorithms must conclude that there's no relationship. But actually, I'm going to tell you that X and Y simply represent two light bulbs where zero is off and one is on. So there's the data for light bulb X and there's the data for light bulb Y. And here corresponds to the fact that both light bulbs are on the zero zero, they're both off the one zero X is on, Y is off, etc. And I now can tell you and reveal that actually there's a hidden switch Z, which also has an on or off position. When it's off, light bulb Y will be in exactly the same state as light bulb X. So if light bulb X is on, light bulb Y will be on.  And when the switch is on, it will be the opposite. So when light bulb X is on, Y will be off. And similarly with where X is off, Y will be on. So the hidden switch provides a completely deterministically causal relationship between X and Y. Once we know the switch value on or off, the value of Y is completely determined by the value of X. And again, we've got the simple causal model and it's completely deterministic in this case if Z is zero, Y is X else, Y is not X. And that's it. Now machine learning from data can never learn that very simple causal relationship, but knowledge with data can. And this example, I mean while everybody knows that you can have correlation without causation, many assume you can't have causation without correlation. And this example proves that you certainly can. So, so what we argue then for is a so-called smart data approach rather than a big data approach. So, you know, if you focus on big data and machine learning, then you're likely to end up with not very, you know, if you're focusing just on the algorithms alone, you might end up with garbage, whereas with a bit of causal knowledge and a smart data approach, you might end up with something useful. So thank you very much. I'm happy to take questions. Good. Thank you very much. I think a really important lessons for anyone dealing with environmental data. I have a question here for you from Chris Stanker, whether you could recommend where subject matter experts who are not statisticians or data scientists can learn how to sketch out their knowledge in a graph to summarize subject matter expert knowledge for the benefit of data-driven colleagues who are not, you know, knowledgeable on base and detect whether biases might be creeping in. Yes, in fact, if I can, I'm not sure, was I, am I still, might just go back to the screenshot. I should have. So, what I didn't do on this. Yeah, so basically the information that I provided there actually covers this, this, this, this very well. So that I assume that people will have access to the, they've got the access to the materials so they'll see these links. Is that, is that right, Keith? Yes. So the point is that there are links there and in the, in the book we actually describe a kind of an idioms, a very simple idioms based approach, which helps you frame that basically helps you get the basis of a causal model in terms of very simple idioms. So for example, the idea that whether or not the, what you're dealing with, what are the underlying hypotheses and which data is unknown and which data is evidence and you have this so-called inaccuracy idiom, which is a very crucial idiom, which actually drives most of the causal models. You'd be surprised how easy it is to draw these causal models once you understand that relationship between an unknown hypothesis, some data that you might observe, and the cause of the inaccuracies of that data. And then you've got, yeah, and then there's, depending on the particular context, so we've got, for example, in legal reasoning, as opposed to other areas like medical reasoning, we have specific idioms, which are related to those subject areas. So that's our guidelines for doing that. And it does help people and we've published papers in the area, not unfortunately an environment, I'm off to say, not an environmental risk, but in the area of medical risk assessment and legal risk assessment. We've actually published papers specifically showing how to help people can use the idioms based approach without, you know, you know, in order to actually build the causal models before you actually do any of the kind of like the Bayesian analysis. Yeah, good. Thank you. I have a question from Alina. So, one has a causal model, but it's not sure on the existence of a particular link, and one wants to test it with some data. Can you do it being consistent with causal rules? So you can. Okay, so remember if there's if there isn't. Because there isn't, remember what we're talking about here in the models that I show in the Bayesian network, just because there isn't a direct link between two nodes doesn't mean that there's not a causal relationship between them. And if you think back to the example that I showed of the cheese, the mozzarella cheese and the engineering doctorates, the correct causal model didn't have doesn't have a direct link between the cheese and the doctorates, right. But nevertheless, there is a causal link via the common explanation. And you'd see that, and you get that by running the model and you can and you'd see that in the data so the model would actually that case would show that there is a causal link even though you haven't got a direct causal link shown in the model so that where so that's that's the answer to part of the question in the case where there really isn't any causal relationship, there really isn't one. And the data shows that there is one then the model is wrong. Okay, so I mean so one of the things that we do if we part of the valid. I mean, nobody is suggesting that that there's a magical way that when we get down with X when we sit down with experts and our kind of knowledge of building these models and their expert domain knowledge that we're going to come up with the right nodes and the right causal direct causal relationships each time we don't. We don't we often find, not only that we missed, we missed out key variables, but we also find that we missed direct calls, we've missed direct calls or relationships which we thought didn't exist. So, and you get that by once you've got some data, right, you can use that to validate the model because you can see whether the model is predicting what the, what the date what where you've got the data for outcomes is predicting so you get that you'll be able to see that and that's why that that's part of the validation process. I have a question here from Neha Joshi. He wants to use machine learning to estimate yield crop yield. And then wants to detect a causal relationship with stress like water pest diseases. I think it's a good question and Berkson's paradox applied to this with those. Look, look, it applies every I tried. People don't. I mean when I mean pearls book is saying that, you know, I recommend people to use before undertake any study like that although I talk about here but what I think pearls book doesn't say succinctly is what I've tried to do in this talk it's those two paradoxes. I'm looking for the founders and looking for evidence of collider bias in the data that you've got, which will screw up any, any causal relationship that you can learn purely from the machine learners you've got to identify those before you collect a lot of data. Right and before you run any machine learning algorithms. It's fine you can run them look in our tool we've got we do machine learning we can you can in our Bayesian network tool once you've got the causal model and that's the crucial thing. You can throw the spreadsheet you can throw the data at it and it will learn the parameters of the of the probability tables. It will do that. Okay. It's when I when we say it's it's learning what the nature of the causal relationship is not whether there is or is not a causal relationship and the danger with simply running a machine learning algorithm with the data that you have is that you're going to learn. It will it will learn a causal relationship, which is just wrong, you know, as we've seen it will you'll end up learning as in those cases that treatment be was better than treatment more effective and treatment a when in reality it was exactly the other way around. This is inevitable. It's inevitable. If you are not. If you if you don't realize that there are some if you unless you've controlled for every confounder and unless you're absolutely convinced there's no possible bias in the way that you've sampled the data, you will run into problems. Yeah, I'll throw one in here. I mean, a very common situation we find in our field in agriculture, for example, is that, you know, there's, there's, there's variables which people simply have no data on at least those economic variables and therefore people simply leave them out of their model, which we know is a kind of big sin and so on. But, you know, your advice would be to incorporate those variables in the model if you were aware from your expert knowledge that there's likely to be a causal association. But then how do you represent that when you've got no data?  You go and get it. I mean, in the case of just those very simple examples of, you know, the Simpsons Paradox example, the confounder, you know, they the the observational study, the data, the original study data didn't have those obvious confounding variables. So you could say, OK, well, once I've got once I realize that there's a confounding variable there, I haven't got the data. Well, I'll go and I'll go out and collect it. But of course, that's not simple. If you've already done the studies, it's very difficult to do that retrospectively. But what you actually find with those sort of confounding variables is that you can get either from expert judgment or from actually a very, very much smaller sample. You don't have to repeat the whole experiment. From much smaller samples, you can get quite convincing evidence about what the effect of the confounder is. It's much easier to it's much easier to get to get that effect from experts and from small data sets than it is to get the information that you needed about the outcomes from those trials. So it's often the case that the things that you're missing are actually the things which people actually have more kind of like real knowledge about and are able to give you subjective judgments which are accurate and can in any case actually be got from relatively small studies. So what we've done in some cases, yes, we've gone out and not just relied on subjective judgments, but done some small surveys or questionnaires to get that missing data. A lot of the way you phrased your problems is in terms of risk assessment, but you know, in many of the problems sort of we face is a question of evaluating whether one intervention is better than another. I mean, we don't really think of that in terms of risk assessment. We really just want to project which interventions likely to be better than another, but presumably that everything you said is just as applicable to. That's the level to that's the level to on on the ladder that's that's and that's exactly what we can do. We can we can simulate the interventions and in fact, you can also, you know, we you I mean an extension of Bayesian networks which incident is also in these in these tools is influence diagrams which enable you to actually so that these would these would have decision nodes, exactly like that, which enable you to see the overall, which are the optimal decisions you can run the model. And there are, you know, again, there's underlying algorithms in there which will compute. Once you've selected suitable utility outcome utility nodes will give you automatically identify what the optimal decision is. Now these models can get very complex, which is why, yeah, it's not necessarily easy to do that but the Bayesian network certainly do give you the framework to do that by being able to model interventions. Yeah. Yeah, I mean one thing. You know that we perhaps it's a trap that we frequently fall into is trying to make models very complicated and they get, you know, quite unmanageable strictly, you know as Bayesian networks. So in cases like that is your advice to kind of simplify, try to simplify the model down to, you know, more basic level and build back up from there or, or start with some what sometimes I know what Sam Savage calls the paper airplane model which is you know very simple models to kind of show that it works before you start to add more more complicated complications into it. Yeah, big supporter of, you know, a starting small we wouldn't have. I mean that the real model that we showed of COVID which incidentally didn't show all of the nodes so it was, it was, I mean it was not a massively complex model but it's certainly not a simple model, but we wouldn't have got to that model. And we not started with the much simpler types of models which I showed earlier so you start with a kind of a six node, you know five or six node model which captures the sort of the key features. And, and then the experts see how that works and then they say are but you know you really need to add this or what and then that's how it develops. Yeah, I'm going to go back to Neil from Kaiser alley which is, could you please tell, tell more about how about big data will I guess when they big data would not be necessarily needed. And where it's not providing enough information about underlying confounding variables. What possible sources of smart data what what would be the possible sources of smart data. There are people who claim to be able to genuinely learn missing data now when I'm going to say missing data this is where there's there's a problem when they'll say that are but we can learn these missing confounders well actually you can't learn them if you're not at least aware of what the substance of those confounders are right. There are lots of fancy machine learning techniques and some of my very close colleagues, you know, do this and can do some quite, you know, interesting and powerful things with missing data. Unfortunately, you cannot learn what you don't what you really don't know. Okay, you will you. And that's the reason why I gave that example at the end, which some people said I was a little bit facetious for the life of no, it is the. If you don't know, if you generally didn't know that there was that missing switch, which is causing that relationship. Nothing you throw out that data, absolutely nothing is going to learn it doesn't matter it doesn't matter what you have to have that that knowledge insight, which simply doesn't come from data. Now, okay, people will go to extremes and say, well, a meta level. They'll say well actually a meta level we've seen these examples before where you've got masses of data like this and there's zero correlation and we're going and we will learn that because there are examples like the light bulb example. And now try out something like a missing switch to see if this works right well, then you'll get it then you'll say yeah then you can try because it might there might be several types of switches or whatever which could create that. But the idea that there was the possible missing switch is not something which you learned from that data. I can argue that you've seen lots of examples like this before but that's that's I think that's stretching it to say that you can learn that from the data. Good. I got a question which probably be the last one from way Iran. Could you please explain more detail in more detail how we determine the structure of the causal graph only based on expert knowledge and common sense.  How can we solve this problem. Okay, so what happens yeah one of the one of the mistakes that people make right is that they'll have in mind the the set of factors that are relevant to their problem and the set of outcomes that they want to predict. And of course, as we know when the causal relationships in these models are very complex right it's it's getting it's being able to minimize we know that as we've seen in the examples that you don't have to have a direct link. To still be able to make the causal inference between them right so the the subtled is how do you how do you minimize a number of links while still retaining those causal dependencies so you're minimizing the direct link. That is the challenge again. There's some ideas in that books for doing that. And the, and the, the idea of using the idioms based approach really does help you here. But another, another idea is to make is to try and group these when you end up with multiple facts to try and group them just grouping them into sort of different concepts you might realize that you can actually simplify the structure by doing that. So there are no simple answers there but there are some heuristics and idiom based approaches which which kind of help. Very good. So I think to me a really important kind of lesson for your environmental scientists coming out of this is that you know if you think a variable is important and you don't have data on it.  Otherwise, you're likely to be in a very rocky place. So I think that's a very good one. I would like to thank you and pass on behalf of myself and the National Environment Research Council for spending time of putting together this talk which has been highly informative and I, it'll be a great resource to have. And I think it's a very recommended reading in my view for anyone. So with that I would like to thank you and pass on behalf of myself and the National Environment Research Council for spending time of putting together this talk which has been highly informative and I, it'll be a great resource to have up on the website for others to look at and I think some really essential lessons there for anyone in this field so I thank you very much again Norman for putting together a very clear and informative presentation and and wishing you all the best for the coming holidays. Yeah. Thank you very much. Thank you very much Norman and thanks everyone for joining us. Thanks Charlotte for all your help as usual. No worries and Merry Christmas everyone.


and deployment tools at VMware Tanzu. And when I'm not passionately debating about packaging abstractions with my team, I enjoy gardening. I love rose bushes, but rose bushes have thorns. And that is what Kubernetes package management is today. It gets us started, but we often prick ourselves. What are these thorns? Well, today I have to install and update my packages manually without being able to use standard Kubernetes APIs, managing multiple packages or packages that have dependencies makes this process even more tedious and error-prone. And other challenges, I don't have a single artifact that I can easily inspect to get a bill of materials of everything that will be installed on my cluster. Overall, I don't have confidence that I know exactly what is running on my cluster and can recreate it anywhere, including in an air-gapped environment. Now, we all value the declarative APIs and layered approach that Kubernetes provides. Why not bring that same goodness to package management on Kubernetes? Similarly, we as the community are embracing standard OCI registry APIs. What if we could leverage OCI concepts like immutable digests to provide guarantees on what exact bits are running on a cluster? Let's keep the beautiful roses and evolve it to be thornless. We have learned that app operators, cluster operators, developers and package authors all benefit from different abstractions. With Carvo, we want users to take advantage of the part of the stack they need. Users don't have to take on the complexity introduced by additional layers and they can easily move up and down the stack when they need to. To enable this, we use app as the lowest level abstraction similar to a pod. And just like deployment builds on top of pod, a package and package install build on top of app. A package repository builds on top of packages. To look at how each of these abstractions actually look like, let's start with a simple application. It is made up of a service and a deployment. I'm going to use an app CR to deploy this app. An app CR represents a group of Kubernetes resources. I can use it to specify how to fetch template and deploy my app. And for each of these steps, I can specify what tool I want to use to accomplish it. I'm using a Git ref here as my source for fetching because I want to keep my app up to date with changes to the Git repo. But Fetch actually supports a variety of different sources including config map, which can be helpful for local development or an OCI image. Similarly for template, we support using YTT and help. We'd love to integrate more tools with your involvement and help in the project. We want to make Carvel really easy to use and extend so you can take it and run with it. Now let's deploy this app. The deploy succeeded and here's my app. Now I want to expose my app to the outside world. I want to use contours HTTP proxy to do that. I would like to consume Contour as a package that someone else has authored. This is where a package repository is helpful. You can think of a package repo as a Maven repo or RPM repo. It holds a collection of packages that are available to install on your cluster. After adding the package repo to my cluster, here are all the packages that are now available to install. I know my app works with version 117.1 of Contour, so let's go ahead and install that. Looking at the Contour package, we see that a package contains configuration metadata and OCI images that tell the package manager how to install itself. Similar to an app CR, package authors can use different tools for fetching, templating and deploying. You'll notice here we fetch using an immutable image reference, a digest. This means when you install this package, you're always going to get exactly the same bits. Now to install this package, I'm going to create a package installed referencing it. When I'm consuming a package authored by someone else, I don't know the details of how that software is fetched, templated and deployed. With a layered approach, we can encapsulate those details away from the consumer, providing a simpler API, while still leveraging the same app CR deploy mechanisms when interacting with the Kubernetes API to deploy the package. After my package install succeeds, I have Contour running. I can now add HTTP proxy provided by Contour to my simple app, check in those changes into my Git repo, and since I have my app CR watching on changes to my Git repo, it will create a HTTP proxy for me. I am now set up for continuous updates and I have confidence in what's running on my cluster. I can drive updates to my app with changes to the Git repo. I can make changes to either my app or the Contour package using standard Kubernetes objects. I know what's running on my cluster is locked down to the contents of an immutable bundle and I can pick up my configuration and recreate the same state on any Kubernetes cluster, including in an air gap environment. We just saw what a package manager that is compatible with GitOps ideas can unlock and yet there is still much more I want to share with you. We have an opportunity to build something really cool and useful with Carvel. Please come join us by trying out the tools. We are very active on Kubernetes Slack and would love to keep in touch with you there. Thank you and have fun at KubeCon.


Hi everyone, Joe for Jasperyscasewrakes.com with the update baseball box, which we're giving away some spots, a buy one, get one, 15 spot giveaway, which fills the break and unlocks the promo, and we're giving away 12 teams in here, so a lot going on, big thanks to this group for making it happen, let's double up, all teams are in, let's roll it, randomize it, three and a three, six the hard way, one, two, three, four, five, and six, six the hard way for the teams, two, three, four, blue jays down at Cubs, and there are the teams right there, there's the results, no trade windows, blah blah blah, let's just roll, I don't think that commonship in this filler box, yeah, no vet commonship, and a Gavin Stone for the Dodgers, black parallel, that's 2.99, Dodgers, that'll be for Joel, and we got a Christian Vasquez to 76 for the twins, that's going to be for Joel, Joey Wendell, Pete Fairbanks to 2023, and Tis Junior black gold, and the relic is Will Smith for the Dodgers, it's either a relic or an auto in these boxes, that's gonna go to Joel on the Dodgers, and do this, do that, and a couple more cards here, Silver Pack, Wainwright, Griffey Junior, Valdez, and Brandon Fott, and that's that, now who's gonna win those those teams, let's gather everybody's names, one through 30, top 12 will get teams, let's roll it, randomize it, two and a six, eight times, one, two, three, four, five, six, seven, and eight, after eight times, there you go, there's the top 12 right there, let's match up with the team, bam, Kevin, you got the Dimebacks Joel with the Reds, Barry with the Dodgers, Alexander with the Blue Jays Joel with the Angels, Kirk with the Tigers, Joel with the Royals, and Marlins Chad with the Pirates, Joel with the Guardians Phillies, and Cardinals, we'll put up five, so you know it's from this update break next to your names, so you know you won those spots from that break, we'll see you in the next video for the break, Jasperyscasebreaks.com, break, break, break.


Lewy bodies are well-known markers of Parkinson's disease. These inclusions, containing tangled fibrils of alpha-synuclein, are typically found in the postmortem brain tissue of patients with Parkinson's. In fact, alpha-synuclein is generally believed to play a central role in Parkinson's and similar pathologies. In a new study, researchers from the BrainMind Institute at EPFL in Switzerland propose a subtle but critical change to this widely held conception. They provide novel insight into the composition and mechanisms of Lewy body formation and show that the process of Lewy body formation, rather than simply alpha-synuclein fibrillization, is one of the key drivers of neurodegeneration. The team began with a popular system that mimics the formation of salt crystals from preformed seeds. Alpha-synuclein in the cell was induced to form fibrils by first adding a small amount of recombinant alpha-synuclein fibrils. This approach has proven incredibly useful in modeling the formation of alpha-synuclein aggregates linked to neurodegenerative diseases like Parkinson's. But where most studies stop at alpha-synuclein fibrillization, which occurs after about two weeks, the EPFL team stood pat. A few more days of waiting, the early findings hinted would be enough to see fibrils giving way to clumps and ultimately to Lewy body-like structures. They were right, at about three weeks those inclusions began to take shape. But then came the challenge faced by others trying to recreate Lewy bodies in the lab, proving that they are in fact Lewy bodies. For that they turned to the proteomic and transcriptomic analyses. The goal was to test their structures for the biomolecular signatures demonstrated by Lewy bodies gathered from brain tissue. They discovered 15 to 20% proteomic overlap between their homegrown bodies and those found in humans. This may not represent a great overlap, but this is not surprising given that Lewy bodies take months or years to form and mature in human brains. The final test was to link these Lewy bodies to signs of neurodegeneration. Correlative light and electron microscopy revealed a cascade of processes beginning when the Lewy body-like inclusions began to form. Newly formed alpha-synuclein aggregates interacted with mitochondria, sequestering membranous organelles including mitochondria, lysosomes and autophagosomes and proteins during the formation and maturation of the Lewy body-like inclusions and leading to severe mitochondria defects and dysfunction. Further imaging suggests that this mitochondria dysfunction could extend to the synaptic scale, overlapping with reductions in synaptic density and dysregulation of the synaptic transcriptome and ultimately causing cell death. Overall the results agree with recent findings reported on Lewy bodies isolated from Parkinson's disease brains. But where previous research offers snapshots of viral evolution, the team's model allows for the reconstruction of the entire process. This makes the model a powerful platform for elucidating the relationship between fibrillation and neurodegeneration in Parkinson's and other neurodegenerative diseases and for screening for drugs that can block this critical process.


We have, in the past, explored some truly remarkable ancient ruins that can be found within Russia. Some of the largest blocks to be found anywhere on Earth can be found within this enormous country. Along with many other intriguing and spectacular finds, still left to be publicly documented, most notably within the notoriously hostile and extremely eerie Euro-Mountain Range, a place long rumored to be the home of the elusive snow Yeti. Left abandoned for untold millennia, laying within a Siberian lake far from modern civilization, rests one of the most scientifically baffling sites to be found anywhere on planet Earth. Claimed to be only a mere 1,300 years old, yet any compelling evidence to back up such predictable claims by certain bodies within historical academia are not forthcoming. Additionally, they harvest no real logical explanation for the site's clear antiquity, reason for abandonment, or indeed construction. It seems with very little to go on, certain groups within constrictive learning practices would like the world to believe that this perplexing site was built a mere 1,000 or so years ago. Known as Poor Bajin, it is a 3.5-hectare artificial man-made island located in a remote, unnamed Siberian lake between the Seyan and Altea ranges, about 3,800 km from Moscow near the Mongolian border. The site was first discovered in 1891, and the purpose of the island has still not been explained over a century later. It is still a mystery for, quote, experts to explain, although they still strongly insist it was constructed no more than 1,400 years ago. An in-depth archaeological exploration took place in 2007, with archaeologists discovering clay tablets of human feet and faded colored drawings on the plaster. These were subsequently used to date the site to quite recently within antiquity, allowing experts to say that the island was built during the period of the Ugyur Kaganate between 744 and 840 AD. However, they severely lack any clear explanation as to what their motives would have been for constructing such a fortress, in such a solitary place so far from trading routes, their own civilization, or indeed anything else of interest. As with many other confusing, and as yet unexplained ruins around the world, the archaeological strata most preferred to academia's currently upheld story in regard to chronological events will always be preferred, and the controversial and often strong evidential datings are ignored or destroyed. Is poor Bajin a far older and once far more advanced site than we are led to believe? With the advances in technology allowing us to venture further and further into the wilderness, it is only a matter of time before a self-funded, inquisitively-minded individual gets a chance to take a really good look at this amazing place. Who built the Great Pyramids? How? Why? Questions many have attempted but seemingly fail to answer, although claimed as tombs with the different internal chambers within the largest, Khufu, named in representation of this purpose. Interestingly, Khufu, or Cheops, is the only one of the three pyramids with internal chambers, the other smaller two merely have tunnels beneath. An enigmatic box, whose lid has long been lost to history, lay within this enormous structure, long claimed to have been the sarcophagus of Khufu. However, although suspiciously small, no one seems to be able to explain how they got it into the chamber in the first place. It is as if the pyramid was built around as it doesn't fit through any of the known entranceways. Since the 19th century, when these chambers were first rediscovered, a tremendous amount of research, though it must be noted, always supervised by official Egyptian antiquity academics, nonetheless remarkable discoveries have at least been partially shared with the world. Most notably, Gontenbrink's door, yet the tomb of Osiris, where this once inaccessible tunnel led, was, once the media was permitted back into the location, found empty, claimed by officials as being found conveniently vacant. A room only discovered thanks to 21st century technology, according to mainstream Egyptologists, was somehow looted. However, there still lay many mysteries within this most intriguing of structures, and we would expect at least one or possibly many more, which no matter how long it takes us to rediscover them, will be too big to hide. For example, although we once thought the tomb Gontenbrink discovered was inaccessible, the chamber at the top of the structure, one of considerable size, estimated at 30 square meters, is so inaccessible. It was only found with technology used to register cosmic rays, a technology usually utilized in high-energy particle physics, capable of tracking particles called muons, produced when cosmic rays strike atoms in the upper atmosphere. These incredibly sensitive detectors were first developed for use in particle accelerators, but they have also been used to gaze into the inner bowels of many geological and ancient artificial features. In December 2015, physicist Kunahiro Morishima of Nagoya University, Japan placed detectors inside the queen's chamber to detect muons passing through the pyramid, thus any large chamber still hidden within the pyramid would be detected due to a higher register of muons than expected from denser angles. The chamber's discovery was cooperated by two other teams of physicists. All three teams observed a large void in the same location above the Grand Gallery. It was a big surprise, says Teyubi. We're really excited, he continued. The researchers say it might even be made up of two or more smaller spaces. Teyubi suggests that it could be, quote, a second Grand Gallery. It is a discovery which we are finding highly compelling.


This study reveals that SCHLAP1 is an oncogene in non-small cell lung cancer, NSELC, and promotes cancer cell immunovasion by regulating the AUF1-PDL1 axis. Targeting of SCHLAP1 may be a potential approach to improve the efficacy of immunotherapy in NSELC patients. This article was authored by Zheng Haidu, Xu Xiannyou, Huang Wang, and others.


The Holy Spirit is the one who helps you to worship, because you can sing without a revelation, you can dance without a revelation, but you cannot worship without a revelation. Because all true worship is a response to revelation. It's the entirety of your being responding to who God is and what He does. And until you've caught a glimpse of who God is, it's not true worship.


Happy birthday, President Daniels. Happy birthday, President Daniels. Happy birthday, President Daniels. We appreciate everything you do for our community and providing us with the opportunity to make connections here on campus. Boilermakers are celebrating all that you do for Purdue University. By continuing to take giant leaps, you've persistently set the bar for higher education across the country. Thank you for another year of dedication to the Purdue community. On behalf of all Boilermakers, we hope you have a wonderful day. Happy birthday, President Daniels.


Friday, October 6th, Dallas, Texas, when we coming down on Combine. Let's go, let's go. He town buggy? Echo lounge, Echo lounge, 7th hill. Hello. Yes. Let's go man. On a Friday? Friday? In Dallas?  What's a Friday? Oh, nigga, guess I'm coming though. He should have never been on a Friday.  The cowboys had to get, they wanted us to do it at the stadium. But I'm not saying that. Not you? Not you? Y'all ain't talking to me? We got to talk to the regular people first. We want to surprise you. OK. This is a surprise.  We're going to go 7 o'clock, 7 o'clock start. Get in there, Dallas. Oh, nigga, you better be there before Jared gets down. I told him 6.  Through? Oh, nigga, please. He said he got the perks.    Oh, yeah. He made me sign the NDA. I can't tell you. A nigga made you sign the NDA. I can't tell you about what we going to have at the meeting. OK. He said, say your money bag.  He been asking about the whole prostitutes. He been asking about the prostitutes. So do you want Jared or not?   Goddamn Dallas. Hey, Lucas Dorchik will be at the motherfucking Echo line, man. Oh, man. Let's go. Dallas. Come on, man. Z coming back. Dallas. Oh, nigga. Let's go. Erica Badu coming. Erica Badu going to be in Dallas? Yeah. Yeah. Ooh, y'all find that from Dallas girl.   If you went to high school, would I ask? Yeah, we need to hear some stories. Some BTS. Hell yeah. How was she in school? How was she?  Yeah, it had to be. How she smell? We need some in-close personal details. $103,000 is going to be there, too. Oh, nigga, he going to be there for real. We got him opening up with a ukulele. He going to go out first. He only speaking through a flute. Hey, Echo Line, 7 o'clock PMD out. He's going to portrait through music through a flute. October 6th. Is it October 6th? Yeah, can you roll it back? Why y'all roll it so fast? I'm going to lift it. Roll it back. Roll it back. That nigga in November. Come on back. God damn. All right, October 6th. Friday, October 6th. Dallas, Texas. Echo Loud Music Hall, 7 o'clock PM. 7 o'clock PM. I come to see. Beating in green. Get your tickets at well. 85southshow.com. This is the family business talk. You got Clayton English. You got Money Bag, Mafia. Nab Green. Only one. Echo Loud. Y'all really don't desire this. Y'all really don't. Y'all really don't desire this. But we don't get it to you anyway. Come on now. Oh, nigga. We know how you Dallas niggas like to gig. Come on now. And when I say Dallas niggas like to gig, be ready to dance off with my boy, Money Bag.  Yeah. That's what you said. You said you want a battle, nigga. You said you could doggie. Okay. All the hoes, we have the wet t-shirt contest. You better dive. Okay. All you bad ladies with your edges, we want to see you come out edge claim. If your edge is better than play, come to the meet and greet and see.    That's you. That's you. We're going to have the gap contest. If you're going to get low like Money Bag, so... Oh, man. We got a Money Bag Needs contest. Bring the stars.  I know you ain't getting lower than Money Bag. That ain't game for us. I'm talking sex. All right, what we got next?


Live from Las Vegas, Nevada, it's theCUBE at IBM Edge 2014. Brought to you by IBM. Hi everybody, we're back at IBM Edge. This is Dave Vellante with Jeff Frick and we're here in Las Vegas at the Sands Convention Center. We're going two days. This is theCUBE, Silicon Angles flagship program. We go out to the events. We extract the signal from the noise. Pamela Gilman is here. She's the manager of data analysis for the services group at the National Center for Atmospheric Research. We're going to geek out on HPC and big data. Pamela, thanks very much for coming on theCUBE. It's great to see you. So tell us a little bit about NCAR and your role there and what you guys got going on. Okay, so the National Center for Atmospheric Research is the National Center that we have the facilities that we provide services to atmospheric researchers. It's run by a university corporation. So I believe at this point we have 95 member universities that define kind of the work that we do. Our focus is mainly on climate as opposed to weather. So most people don't understand the difference there. Weather is what you listen to your forecast at the evening, where it's short-term, you want to know what it's going to do tomorrow. Can I go to the pool today or not? Right. Climate is a very long-term research. So some of our researchers run models that they're looking at what happened in the weather over a thousand-year time period. And so they're looking for the cyclic things, they're looking for the correlations from year to year and through time. And so what the National Center for Atmospheric Research does is provide the resources to allow that type of research to occur. And you're an independent agency, is that right? We are federally funded, but we're managed. UCAR is the University Corporation for Atmospheric Research, which is the consortium of universities and that's our governing body. So what's happening with climate? That was, thank you for describing that climate versus weather and it's something we see all the time. I see the left and the right is arguing about it. What's the facts? What's really happening with climate? So the facts are a little difficult to find sometimes. Science is, especially with something that you can't produce an experiment and then say, okay, I've proven it. I've done it three times and I get the same answer. Climate is a little more difficult than that. So some of our researchers, one of the groups that I work with that I love to work with does paleo-era climate. So they've actually gone back and they're running that thousand years across the paleolithic era. Looking at the data from, you can pull core samples and see what actually occurred. And so they're looking to see, do the models that we run to look at things in the future, were they able to show you what actually happened in that time period? So they're trying to look at what they think can occur and what's happening. And of course, depending on how you run it, you can get different answers. I believe that at this point there, the majority of scientists believe that we're in a period of change. Things are changing rapidly. We've got, if you look at the news any day today, you see the ice sheets are melting and then you have where you get a warmer day one day and a colder day the others. So mostly this is trying to figure out why is the change occurring? Is this the normal change? And coming to some consensus as to what's happening. I saw Mount St. Helens blow up about 30 years ago, approximately, and when Mother Nature decides to do something or rip Africa from South America, it's pretty significant. That question on what you guys do is that the data, is it the data sets? Is it the computing horsepower? Is it the models when you say you provide solutions for people to test things to the universities? What exactly do you guys provide and where do you get that? So we do a little bit of all of it actually. So we have the, from the scientific standpoint, NCAR is built of multiple labs and there's five or six labs and all but one of those labs do the science. And so there are two groups that I work with closely that do the models that are one is the producing that Paleolithic and the future climate data. Another one is one that does a weather model that does the hurricane forecasting and we work with that. So we have the scientists there that are generating these models that they run. The lab that I work in is the Computational Information Sciences Lab. We actually produce the resources and we manage those resources. So we have a very large supercomputer center. It's about 25,000 square feet today. We've got the double capacity that all we have to do is put machinery in it to do that. And that's where our flagship systems live today. That current system is an Big Eye Dataplex system and that's where all of the storage systems, we have a large tape archive. So everything from spinning storage into tape that supports that supercomputer. And then the university will lease out? They actually just ask through the allocation. There's an allocation process and we have a panel that decides so they'll say this is the science I want to do, this is kind of the research that I'm trying to look at and can I have time on the computer? And what are your data sources? Where's all this data come from? So a lot, most of the data is produced either on our computer, in our center or at some of the other national centers.  And so one of the focuses that my group works on is some of those data transfer protocols to the spinning storage is so that when you produce data elsewhere that we want to have an NCAR that we can get that there effectively and efficiently. How much data are we talking about here? So currently what we have in the facility is about 33 petabytes in our tape archive. We have about 18 petabytes of spinning storage that's available and that's it anywhere from 60 to 90% used at any given time. Okay and any flash in there? We don't have any flash today, that's actually a technology that we're very interested in moving forward. What do you envision using flash for? So metadata or certain analysis? So we've looked at metadata in the past right now, it's more efficient for us to keep the metadata in the spinning storage with the data itself. I think the thing that we're looking at, we've got a next generation machine in the next couple of years is looking at the flash as kind of the burst buffer, the terminology that everybody's starting to talk about now. Our models do a lot of small file output so they're because they run through time they all run one time step in output data, run a time step output data. And so having that flash that's close to where they're producing the data that can handle that as they do these time steps and then allowing that to trickle out more to the spinning storage over time is something that we're very interested in looking at. So there's definitely a discussion, we talk about it all the time in theCUBE with this whole notion of high performance computing and big data coming together and actually finding commercial applications. I wonder if we could talk a little bit about sort of the big data HPC, big data meets HPC. What are you seeing there? Is it where do analytics fit in? How does it affect architectures? Talk about that a little bit. So one of the projects that I've worked with and I think a lot of people have heard about is the IPCC runs, the International Panel for Climate Control. And they do these periodic runs every four to five years that produce and it's an international panel. And so the first year that I worked with that was the IPCC four run. And the total amount of data that we output from that run was 100 terabytes. That was not difficult to manage. I was able to give them a dedicated storage system. That data once it's produced is made available to the community for about five years for anybody to come and get and download to do whatever research they're doing with. So we completed the IPCC five run a couple of years ago and NCAR can't even curate all of the data from that run. So we have about a petabyte to two petabytes of that data and that's not even all of it from that run. So you can see the scale within a four year time frame of how much just the same thing you're trying to do could jump. And so that's a huge challenge. So we use some of the, we bring that data in and then we have what we call science gateways that hosts that. And so that's a way of allowing the analytics to occur to allow the community to come in and say, I want the data from this run but some of these things have like a hundred variables in them. I only want the three variables I'm interested in. And so we've taken and coupled that functionality with our computational side so that the group that manages that data, they can pull that, use our computational resources to pull those variables out, package a data set and deliver that to the customer that's asked for that. So you're obviously a GPFS user. Yes. Right. I have been for a very long time actually. Tried and true file system. You're seeing it now seep in. I talked about commercial applications. You're seeing that seep into sort of IBM software defined storage. What other changes can we expect in because there's so much data now, so much demand for real time. Huge costs associated with all this stuff. What architectural changes should we expect coming down the pike in the next four to seven years? Four to seven years. Everything will change in seven years. The challenge is figuring out how you can change with it I think. One of the things that we've already done is we used to have what I always referred to as islands and we've talked about that in the past of being a very data centric way of looking at things versus a resource centric way.  They produced a lot of data. Their job was to have a fast machine and be able to put out data as quickly as possible. And then it was somebody else's problem. And that data moved around from resource to resource for each of those tasks. The thing that we did with the data center that we have now is we pulled all those resources into a central pool. So what we're trying to do is shift to where as the data is produced somebody can look at it and they don't have to move it. It's all right there, it can live in one home and they can get that complete task done. And it's what we've referred to in some of our talks lately as an information centric model of trying to get the user to move what they're doing to where the data is because we can't move the data any longer. So what I want to see in the future is a more tight or coupling of that. And hopefully analytics that can occur during computation. Right now it's still a very separate step. You do the compute and it's still different groups of people. They produce that and then somebody else looks at it. Is it really like to see if we can provide an architecture where that can all happen in stream. I think the flash plays into that is if we can keep some of that in memory and they can do post-processing or analysis, work on it while it's still there before it ever goes to spinning storage, then we can speed up that workflow. I mean conceptually it sounds very Hadoop-like. Right, ship the code to the data not the data to the code. Exactly. Using Hadoop? No we aren't using Hadoop. The way that our codes are structured don't really work well with that at this point. But we do have an effort underway right now to look at the codes and see if we can get the codes to shift somewhat to use some of those newer technologies. Conceptually it's a similar philosophy. Yes, very much. Ship five megabytes of data to petabytes of code to petabytes of data not to reverse. Exactly. And so it sounds like there's still a lot of batch activity going on. Yes. And you're trying to make it more real time. I mean I see parallels with the whole Hadoop meme. Yeah, exactly. Big data and analytics.  It's actually like six pieces of code that have to talk together. So very fragmented. Very. And so it's the challenge right now with the software developers with that is figuring out how to use some of the newer techniques. One of my challenges is because I am a GPFS person is watching what they do bad to my file systems and to my storage and trying to find the techniques, the coding techniques that I can pass on to them and say, here's a better way to do this. And you'll find that what you're trying to do is going to run more efficiently and faster if you bring in some of the parallel IO techniques that especially GPFS really likes. So Pamela, you talk about a lot of the data that you guys produce is produced by your models that are going out. What about on the inbound side? The other kind of big trend that we talk a lot about on theCUBE is the internet of things and the industrial internet and all these sensors that are out all over the place. I would imagine in a climate control or a climate research world that's a great opportunity to bring in lots of new data that maybe didn't have access before or not as easily. Are you guys taking advantage of that? Is that kind of growing the input side of your processes or still not so significant? To some extent, we are doing more what we would refer to as observational data. So there's instruments that go up in airplanes and collect data and we're bringing more of that in. That's really more of the weather side of things versus that climate side. So we're predominantly data that we produce, but we are looking at and like I was referred to earlier the data transfer techniques and making sure we have the bandwidths and the things that can handle that is we're looking at because the systems are expensive these days and having a system that's large enough to do the work we need to do means that we work in more locations than what we can afford. So that's becoming more and more the challenge is to how do I bring a 10 terabyte data set or a 20 terabyte data set that I produced at another site back because it's the researchers where we are that need that data.  You know, make performance consistent. It doesn't have to be the best, but don't lose the data. How do your requirements differ from the commercial world? So we very much have a don't lose the data and we've just been through an incident where it was please don't lose the data. We do push performance and so it is a real challenge for my team is how do I get the maximum performance because then I, that's where my money has gone. So how do I get that maximum and still have the stability? One of the things that I absolutely love about GPFS is that you can push the performance and remain stable. And it's been one of the things, we've used that software for 15 years at least at this point. And so it's been a real highlight. We do not back up a lot of our data. So unlike the commercial world where the center itself is taking that responsibility, dual copies, three copies, you know, tape, our tape archive is there for our users to use and it's their responsibility to do that. A lot of the data could be reproduced, which I think is different than a business world is because it's model data, you could rerun the model and produce it. That's expensive to do. So it's that balancing act of how much do we allow of tape archive so that if it's really critical they can have it, or do we just say, maybe it's more efficient for you to reproduce it. So we're seeing some of the tenets of high performance computing seep into the enterprise, certainly scale out, low cost to guys that use commodity components for years and years and years, certainly we're seeing GPFS. How do you back up a petabyte? You don't. You don't. So a lot of learnings here. Good luck. Pamela, thanks very much for coming on theCUBE. It was really a pleasure having you and good luck with the project. Thank you very much. Keep it right there, everybody. We'll be right back with our next guest. We're live from Las Vegas. We're at IBM Edge and this is theCUBE. We'll be right back.


watching Beyond Grenades today and today I have a very special video for all the fellas watching. I know it's been a while since I gave you all video but you are gonna love this new invention I'm going to show you. The twisted up comb. This right here is going to be your new twisting friend when it comes to doing your hair. But in order for me to show you how amazing this twisted up comb is I'm going to need a volunteer. Well would you look at that? We have a celebrity guest today welcome Devin Fuller. Devin Fuller is one of the receivers on the Atlanta Falcons. So you know that one football team that made to the Super Bowl? No big deal. Want to say what's up to everybody? How you doing guys? So we're gonna be doing this hair today using the twisted up comb. So there's a couple ways that you could use the comb. You actually could hold it all the way around the side like this or you could use the actual handle and then go like this whatever is more comfortable for you.  For the best results you should use the comb on your hair in its dry and natural state. You don't want your hair wet or too soft and the one to reach your desired twist look you have the option of then adding a styling product like a pomade. This will only work on hair similar to Devin's meaning a short or a little bit longer type of afro texture. This will not work on straight hair. The comb I'm using is their black and silver comb but there will be new colors available at TwistedUpComb.com. Alright so after just a couple minutes I think we are done so now let's get a close-up and see how well it worked. Having twisted up comb definitely gave my hair more defined twists and having it small and compact would definitely ease you on my head in this bunch. Speaking of the size of the comb it's actually so small and portable that you could fit this in your pocket. Yes let's try it. Okay so I want you to put the twisted up comb in your pocket.  Do a little fashion show, a little twist. Yeah so which one would you probably walk around with? Now we know. So now that you know how amazing the twisted up comb is let me tell you where to buy. You can get it at TwistedUpComb.com and on their website it'll be $24.95 and because you guys watch this video of course I'm gonna hook you up.  So I'll put all this below in the description box with the link to the website and the promo code. Another cool thing about the twisted up comb is that there is a lifetime warranty available. So if you want to spend an extra $10 you can get this replaced as many times as you need. Lifetime guarantee, no questions asked.  And another thing to keep in mind is that when you go to a barber shop think about if they use that sponge in the shop. Like how would you feel if he used the sponge on somebody and then used it on your hair too? Yeah not thinking about it, what's happened to me. Really? Yeah it's kind of gross. You have no idea how many heads that sponge has touched but this if you have it in a barber shop or if you bring in your own it is super sanitary. It's really easy to clean. You also could see more videos on how this is used on long or shorter hair if you go to their Instagram which is twisted up. So that is everything you need to know about the twisted up comb. It is durable, it is portable, it is sanitary and if you guys enjoyed this video of course make sure to give it a thumbs up and subscribe. Share it with your other friends that also use the sponge because you know they're gonna want to see how this works. Devin thank you for being my guest today and this one is for you. Thank you. This is gonna be great on the road. Definitely and you also could follow us both on our social media with your social media. Full of love 87. And you follow me at Ms. Bianca Renee on Instagram, Twitter and Snapchat. I post new curly hair videos every Sunday so I hope to see you all next week. Thanks for watching Bianca Renee today.


Thank you, Margo. And thank you to our wonderful panel, Tiara, Jessica, Noel, and Shin. We're so grateful to you for sharing your insights to conclude day one of the C3E Women in Clean Energy Symposium and Awards. Thanks also to our first panel with Shalanda, Carrie, Melanie, and Amy, and to Cam Moeller and Secretary Granholm for kicking off the symposium this morning. I'd also like to take a moment to acknowledge our sponsors without whom C3E's work would not be possible. And thank you to the audience for your attention, great questions, lively chat. I hope you feel as enlightened and inspired as I do right now. And we look forward to another day of our symposium tomorrow. Please join us at 9 a.m. Pacific Time when we'll present the Lifetime Achievement Award and the mid-career awards for entrepreneurship, law and finance, technology, research, and innovation, and social, economic, and policy innovation. We'll also have two panels to explore big topics on how corporate leaders can advance social equity and environmental justice in the energy transition and energy access in developing economists. Enjoy the rest of your afternoon or evening, and we look forward to seeing you all tomorrow. Goodbye.