From around the globe, it's theCUBE with digital coverage of Red Hat Summit 2020, brought to you by Red Hat. Welcome back to theCUBE's coverage of Red Hat Summit 2020. Of course, this year it's rather than all coming to San Francisco, we are talking to Red Hat executives, their partners and their customers, where they are around the globe. Happy to welcome back one of our CUBE alumni, Lisa Spellman, who's a corporate vice president and general manager of the Intel Xeon memory group. Lisa, thanks so much for joining us and where are you joining us from? Well, thank you for having me and I'm a little further north than where the conference was going to be held. So I'm in Portland, Oregon right now. Excellent. Yeah, we've had customers from around the globe as part of the CUBE coverage here. And of course, you're near the mothership of Intel. So Lisa, let's start of course, the Red Hat partnership, I've seen the Intel executives on the keynote stage for many years. So talk about, to start us off, the Intel Red Hat partnership as it stands today in 2020. Yeah, you know, on the keynote stage for many years and then actually again this year. So despite the virtual nature of the event that we're having, we're trying to still show up together and demonstrate together to our customers and our developer community, really give them a sense for all the work that we're doing across the important transformations that are happening in the industry. So we view this partnership and this event as important ways for us to connect and make sure that we have a chance to really share where we're going next and gather feedback on where our customers and that developer community need us to go together because it is a rich long history of partnership of the combination of our hardware work and the open source software work that we do with Red Hat. And we see that every year increasing in value as we expand to more workloads and more market segments that we can help with our technology. Yeah, well, Lisa, you know, we've seen on theCUBE for many years, Intel strong partnerships across the industry from the data centers, from the cloud. I think we're going to talk a little bit about Edge for this discussion too. So Edge and 5G, I think about all the hard work that Intel does, especially with its partnership, you know, you talked about, you know, I think back to the early days of Red Hat, you know, the operating system things that were done as virtualization rolled out. There's accelerations that gone through. So when it comes to Edge and 5G, obviously big mega waves that we spend a lot of talking about, what's Intel's piece? Obviously we know Intel chips go everywhere, but when it comes to kind of the engineering work that gets done, what are some of the pieces that Intel's working on? Yeah, that's a great example actually of what we are seeing is this expansion of areas of workloads and investment and opportunity that we face. So as we move forward into 5G becoming not the theoretical next thing, but actually the thing that is starting to be deployed and transformed, you can see a bunch of underlying work that Intel and Red Hat have done together in order to make that a reality. So you look at the move from a very proprietary ASIC-based type of workload with a single function running on it. And what we've done is drive to have the virtualization capabilities that took over and provided so much value in the cloud data center also apply to the 5G network. So the move to network function virtualization and software to find networking and a lot of value being derived from the opportunity to run that on open source standard and have that open source community really come together to make it easier and faster to deploy those technologies and also to get good SLAs and quality of service while you're driving down your overall total cost of ownership. We've spent years working on that together in the 5G space and network space in general and now it's really starting to take off and then that is very well connected to the edge. So if you think about the edge as this point of content creation of where the action's happening and you start to think through how much of the compute or the value can I get out at the edge without everything having to go all the way back to the data center. You start to again see how those open standards in very complex environments can help people manage their total cost of ownership and the complexity. All right, Lisa. So when you're talking about edge solutions when I've been talking to Red Hat where their first deployments have really been talking to the service providers really I've seen as an extension of what you were talking about network functions virtualization. Everybody talks about edges. There's a lot of different edges out there. The service providers being the first place we see things but all the way out even to the consumer edge and the device edge where Intel may or may not have some devices there. So help us understand where you're sitting and where should we be looking as these technologies work? You know, it's a great point. We see the edge being developed by multiple types of organizations. So yes, the service providers are obviously there in so much as they already even own the location points out there. If you think of all the myriad of polls with the base stations and everything that's out there that's a tremendous asset to capitalize on. You also see our cloud service provider customers moving towards the edge as well as they think of new developer services and capabilities. And of course you see the enterprise edge coming in. If you think of factory type of utilization methodologies or in manufacturing all of those are very enterprise based and are really focused on not that consumer edge but on the B2B edge or the infrastructure edge is what you might think of it as but they're working through how do they add efficiency capability, automation all into their existing work but making it better. So Intel the way that we look at that is it's all opportunities to provide the right foundation for that. So when we look at the silicon products that we develop we gather requirements from that entire landscape and then we work through our silicon portfolio. We have our portfolio really focused on the movement, the storage and the processing of data. And we try to look at that in a very holistic way and decide where the capability will best serve that workload. So you do have a choice at times whether some new feature or capability goes into the CPU or the Xeon engine or you could think about whether that would be better served by being added into a smart nick type of capability. And so those are just small examples of how we look at the entirety of the data flow in the edge and what the use case is and then we utilize that to inform how we improve the silicon and where we add features. Well, Lisa as you were going through this it makes me also think about one of the other big mega waves out there artificial intelligence. So lots of discussion as you were saying what goes where, how we think about it cloud edge devices. So how does AI intersect with this whole discussion of edge that we were just having?  But so AI is such an exciting capability that is coming through everywhere, literally from the edge through the core network into the cloud and you see it infiltrating every single workload across the enterprise across cloud service providers across the network service providers. So it is truly on its way to being completely pervasive. And so again, that presents the same opportunity for us. So if you look at your silicon portfolio you need to be able to address artificial intelligence all the way from the edge to the cloud. And that can mean adding silicon capabilities that can handle milliwatts like ruggedized, super low power super long life, you know, literally out at the edge. And then all the way back to the data center where you're going for much higher powered higher capability for training of the models. So we have built out a portfolio that addresses all of that. And one of the interesting things about the edge is people always think of it as a low compute area. So they think of it as data collection, but more and more of that data collection is also having a great benefit from being able to do an amount of compute and inference out at the edge. So we see a tremendous amount of actual Xeon product being deployed out at the edge because of the need to actually deliver quite high powered compute right there. And that's improving customer experiences and it's changing use cases through again, healthcare, manufacturing, automotive. You see it in all the major fast mover edge industries. Yeah, now really good points they make there, Lisa. We all got used to limitless compute in the cloud and therefore, let's put everything there. But of course we understand there's this little thing called the speed of light that makes it that much of the information that is collected at the edge can't go beyond it. I saw a great presentation actually last year talking about the geosynchronous satellites. They collect so much information and you can't just beam it back and forth. So I better have some compute there. So we've known for a long time that the challenge of our day has been distributed architectures and edge just changes that, the landscape and the surface area that we need to touch so much more. When I think about all those areas, obviously security is an area that comes up. So how does Intel and its partners make sure that no matter where my data is and you talk about the various memory that security is still considered at each aspect of the environment? Oh, it's a huge focus.   Everything is smart the entire way through the lifecycle. And so with that smartness, you need to have security embedded from the get go into that workflow. And what people need to understand as they undergo their edge deployments and start that work is that your obligation for the security of that data begins the minute you collect that data. It doesn't start when it's back to the cloud or back in the data center. So you own it and need to be on it from the beginning. So we work across our Silicon portfolio and then our software ecosystem to think through it in terms of that entire pipeline of the data movement and making sure that there's not breakdowns in each of the handoff chains. It's a really complex problem. And it is not one that Intel is able to solve alone nor any individual Silicon or software vendor along the way. And I will say that some of the security work over the past couple of years has led to a bringing together of the industry to address problems together whether they be on any other given day a friend or a foe. When it comes to security, I feel like I've seen just an amazing increase over the past two, two and a half years on the collaboration to solve these problems together. And ultimately, I think that leads to a better experience for our users and for our customers. So we are investing in it not just at the new features from the Silicon perspective but in also understanding newer and more advanced threat or attack surfaces that can happen inside of the Silicon or the software component. All right, so Lisa, final question I have for you. I want to circle back to where we started. It's Red Hat Summit this week, long partnerships. As I mentioned, we see Intel at all the cloud shows you partner with all the hardware software providers and the like. So big message from Red Hat is the open hybrid cloud to talk about how that fits in with everything that Intel is doing. It's an area of a really strong interconnection between us and Red Hat because we have a vision of that open hybrid cloud that is very well aligned. And the part about it is that it is rooted not just in here's my feature, here's my feature from either one of us. It's rooted in what our customers need and what we see our enterprise customers driving towards that desire to utilize the cloud to improve their capabilities and services, but also maintain that capability inside their own house as well so that they have really viable workload transformation, they have opportunities for their total cost of ownership and can fundamentally use technology to drive their business forward. All right, well, Lisa Spellman, thank you so much for all the update from Intel and definitely look forward to seeing the breakouts, the keynotes and the like. Yes, me too. All right, lots more coverage here from theCUBE, Red Hat Summit 2020, I'm Stu Miniman and thanks as always for watching.


Hi everyone, I have here for you another example of finding the area between two curves. So here we're going to find the area of the region bounded by the graphs of f of x equals 2 minus x squared and g of x equals x. So let's take a look at them on our graphing calculator and just go ahead and do zoom 6 to graph them and this is really your classic area between two curves problem. Now you'll notice one thing that was not mentioned in the problem are any kind of boundaries or limits of integration. Those were going to have to get from the graph so you'll need to go ahead and find those points of intersection. I shall leave that for you. I've already done it and I know what it is so they should be negative 2 on the left down in the third quadrant and then x equals positive 1 up there in the first quadrant. Now think about the orientation of your representative rectangle. If you want that rectangle to hit both curves it needs to go vertically. Therefore it's going to be a dx problem. Therefore your limits of integration need to be the x values from those points of intersection. But please do try it to make sure you get x equals negative 2 and x equals positive 1. So let's go ahead and set up our integral. So our area then will be the integral from negative 2 to 1 of remember the 2 minus x square curve was on the top. Remember top minus bottom minus the bottom curve which was x. So we can go ahead and do our anti-differentiation and evaluating that from negative 2 to 1. So you can do that by hand. I'm going to go ahead and use function integral on my calculator. So math 9 from negative 2 to 1. So I'm going to do this a little bit faster. I know that my upper curve the 2 minus x squared is what I had under y equals under y1. So if you hit your vars button go across the top to y vars and under function you can pull up y1 that way. So just a little bit faster saves you a little bit of typing. Then we can do the same thing to pull up y2 which is where the other function was. Just hit 2 so it looks like that. So the answer we get a nice 4 and a half. So once again we could check that by using that area between program. So let's get that a try. So our lower boundary in this case is negative 2. Upper boundary is 1 and voila there's the answer. Again a really great way to check your answers.


So, like last year's budget theme was Sapthirishi and Amritkaal which includes many sectors such as inclusive development, youth power, health sector and many more. So, the most biggest disadvantages of last year's budget which has not been properly allocated till now is the revenue deficit which they are having. The revenue deficit created a big impact in the proper allocation of budget in every sectors and especially on education sectors. So, we wish that this time the budget will give a proper emphasis and proper impact on the current education development of the students, not on educational growth but on educational development to provide them quality education along with mental health awareness and physical health awareness. As we have a commerce background, our education will have a huge impact on the budget which will give us 4.28 million crores. So, if we increase the budget, we will be able to add significant amounts of money. So, our education will be able to develop in a way that we can do in the next few minutes. If you liked our video, don't forget to like, share and subscribe to our channel.


Everybody in the mechanical engineering program do what's called a capstone project and a capstone project is essentially trying to take all the knowledge you gained, how to design mechanical systems, transfer of energy, thermodynamics, fluids, all that and try and put it together into something physical, something visceral, something that works. To me the best one available here is the form of the car team. We start with absolutely nothing and we build a race car so we go through the whole design process, manufacturing, production of it and then testing and ultimately competing in high performance open wheel racing. So this Sunday we took out the 2012 car and that has been our best performing car competition ever. We've been in this car as a competition. We're racing against about 130 other schools. We compete against them as this one was one of the best. So we're getting some really good runs in. We had a couple of drivers. We had one semi-pro driver from University of Brown Baltimore College who was driving it and he was driving it absolutely beautifully. This project is absolutely the best thing that I encounter at the Academy. It's the best combination between learning as an engineer and learning as a leader. To a man and woman they always tell me as they're leaving this program that they learn so much more by applying it in a real world that you always want this. So that's probably the biggest thing I want them to take with them into the fleet.


That looks so cool! Hello everybody and welcome back to another episode of Jurassic World Evolution 2! Where today, as you can quite clearly see on the screen, we have a beautiful cinematic camera going on. So what are we going to be doing in today's video? Well, we're going to be looking at the new updates that have came to Jurassic World Evolution 2. And more specifically right now, we're going to be focusing on the four new dinosaurs that have been released. That being Yutoranus, the Deenokyrus, the Sauror, the little squirrel dinosaur thing, and then the flying bat thing. I don't know what that one's called feature. We're going to be checking out their different animations, killing animations for goats and prey animals like we just saw. Oh, we had Go-Kam for a second! As well as seeing their battles with each other and see if there's any cool interactions that we can really check out. Because that's what we want to see. We see new sort of dinosaurs, new creatures come to the game. We want to see, right? We are back in the Malta Research Facility. Why? Because I very re- Oh, look at that! What a cuties! They're all wearing the same shirt. So this is kind of like a cult. Oh dear. Anyway, no, this is going to be a lovely time. I mean, we've been waiting for an update for the longest time. And finally we're here. And what we're looking at right now, this is the free update. Even if you don't buy the DLC, you will get a free update. And this allows, for one, what you're seeing right here. And that is a cinematic camera. Normally when you click see, you can zoom in and you go into camera view mode and stuff like that. New manuals, change things, roll the camera and stuff like that. Now, you have a cinematic camera. So if I click G, the content come up. So you can actually set a timer for how long you want your park to be on despite. You can also change certain things.  Loads of different things. So if we go into a manual, there you go. So these are the classic ones that have always been there. Take a trip to Jurassic World. Greetings from Jurassic Park. I don't know, maybe these are new actually. I don't remember post cover stamps. That's kind of cool. I like this. Film logos. I mean, I guess they're there. Sure. Letter box different than if you're a Peter Jackson or whatever. All that jazz we're putting it on. You've got your filters now. I think these might be new as well. I feel like the night vision. I mean, we have to have that. That is a great addition. CP and grayscale. Yeah, they're cool too. And then guidelines. Of course, you've got the rule of thirds and nothing else. Now, if we go into here, you can set a time for long. So if you just want to be set on a certain view for a long period of time, you can also different things. You've got zip lines as well. You all these different selections that you can choose from. Camera movement can be manual automatic. So it will just stay on one or you can kind of do a nice pan. That's completely up to you. And just another absolutely brilliant way of, you know, adding something into this game that you don't necessarily need anything to do. There's no extra features added. I mean, maybe they've added a few cameras to cycle through depending on what your park is. Maybe they've added for certain islands and stuff like that. But it's just, I mean, this is already a beautiful looking game. We can't deny it. And now we've got this amazing way to view our dinosaurs. And that's what we really want, right? We want new ways. Maybe you don't want to play. You're sticking on the background. I don't know why you would if it's screensaver. Let's be honest. Let's do my little computer in. He's going... The whole time. There you go.  It's gone again. But yes, so we are here. And we may as well get into the meter bones of today's video. So Dino Kairis being the big herbivore, Yutaranus being the feather convo. There was a lot of speculation as to whether we would actually have different ages. Like baby Yutaranus because of one picture. But it was just apparently a scale. They basically done that and they'd put it like all the way on the floor. So it looked like some things were really big or small in the background, but actually they were big. And that's apparently that's what caused it. I personally think they scaled some down. They have the ability. It's a little Easter egg for future. But let's see the Dino Kairis' intro. Oh, wait, hold on. Before we do that, let's have a look at Species Viewer, shall we? So these are the normal looking things in the skins. Very interesting. Let's have a look to have a quick glance through. Ooh, that's a nice one. That one's the Chaplain Valley. Okay. Ooh, the Quillian Mountains are nice one too. Salvavard's also looking quite nice. So let's check out the... Yeah, kind of Yukon. Okay. And then the different sort of patterns. Ah, so there's two variations. Ah, I like it. So you've got inverting stripes, but you will always have a stripe, I think. I mean, there's blank patterns. So if you're just fancy, no stripes. So you've got stripe inverted stripe and then not stripe. Anyway, let's get back. Let's get back and talk to you soon. Without further ado, let's release our first ever official... Dinochirus. Oh, look at that waddle. Oh, it's got big chunky toenails. Look at you. It doesn't have so much of a beak. It's like a... It'll like giant lips. Oh, are these ones all the same? I'm pretty sure they're not. No, they're not. But those two look very similar. There we go. This downy dinosaur is often likened to an ostrich. Not surprising, I suppose, considering the long neck, the feathers, the beak. It's just a lot bigger in size, huh? But if things go awry, something tells me this one doesn't need to bury its head in the sand. Oh, okay. So we've got Cabot Finch kind of confirming that this thing is going to be able to fight back against carnivores, and I cannot wait with this little short dumpy arms. Look at it there. Really.  They're things not to be messed with, especially for the inexperienced carnival. So, oh, oh, oh, I thought we were going to get the animation of them interacting with each other, but I think, is this it? No. Let's see if we can get that one on the go. Oh, look at the little screechy scratches. Have you got fleas? Bet you do. If I'm honest, this isn't the dinosaur that I'm most excited about when it comes to this DLC. More excited about the Utoranus. I feel like I've seen a lot of Dinochiruses or, you know, feathered sort of Therizonosaurus looking creatures. But actually, this decides it's okay. It's an okay design. I think I prefer the one where it's more of a beak-looking kind of thing. Maybe this one's more scientific accurate, but then again, this is Jurassic World. Let's be honest, we're not really expecting scientific accuracy here.  Oh, dude, that territorial. Oh, this is a fight. This isn't an interaction. Oh, oh, okay. Right, well, there you have it. That guy won. Ooh, let's have a look. What do they need? They eat fish, and they're also plant eaters. Their population is fine, but I guess they just need more things.  Apart from, you know, the usual of it killing guests, which is just running them over and them going miles. But because this is a fish eater, a pisco bomb, we will get to see some cool, oh, that's very judgy stays. Like you're drinking by water. Anybody want a fish?  Oh, here we go. Ooh, oh nice. Didn't even use the claws at all. They're just straight in with the dive to get a fish. Love how the other dude of course is blocking the way. Really cool.  Apparently everyone's really hungry. They're all going to be eating fish at the same time. Maybe we'll get a cool interaction. Oh. Oh, that is brilliant. That is my fish. I love how the furs sort of interacting with the wind. It really gives this sort of feel, a real feel of this environment. It doesn't look stale. And, oh, I thought that was a different claw, but it wasn't though. They seem to always just go for their faces to catch these fish. Interesting. But with the fur sort of vibrating, reverberating the way it is, it really does give a sense of, damn, that's high gust winds right here. It doesn't look it otherwise. This guy really doesn't like the other one eating its fish, which is very interesting. But, hey, oh, this is cool. Just look at them go. It's a shame we're not getting any extra claw slashes from the fish like scooping, even though they do seem to have relatively different sorts of eating animations. But it is all just sort of balancing with the claws on the side of the feed as if any balance goes on. And then, oh, I love that. I love this sort of... It's always the one to the left. Doesn't let the one to the right. So they balance with the claws and then they use their mouth really to just pluck up little bits of fish. Oh, that was like a diamond fish. That looked like something from Legend of Zelda, a crew of diamonds. But, again, a really cool animation and another one there with the claws. Now, with that being said, I think there's one more thing left to do, and that is, of course, see what they're like against an Alasaurus. Is it going to hunt it, or is it actually going to fight it? Oh, it looks like we have all a slashy fight already. Okay, Alasaurus is winning. Oh, that slash from the Therizinosaurus. Very similar. I was going to say very similar to the Therizinosaurus, but yes, no. It's very similar to the Therizinosaurus. And this is from Dino Kars. Okay. The Alasaurus did win that one. Oh, and another thing I completely forgot about this is they have finally, finally added slow motion into this. So you've got like a final battle scene between Simba and Scar. This has been, I think in the game for a long time, like, slow-mo has been an option with capture mode, but it never seemed to work. But finally, at least as far as I'm aware, they've added it in, and it works. Like, look at that. Oh, the fight looks so cool. Yeah, you need that. Like, get some protocols of Narnia. Final battle music here. It'd be brilliant. Oh, here we go. Is this going to be it? A raw lunch hit? Oh, no, it's not. No! It just falls over and dies. I was expecting something a bit more interesting. Damn. Damn. Oh, that's a shame. I'm assuming then, if Dino Kars does a fight, attacks the Alasaurus, it's probably just going to do that, isn't it? I get the feeling it is. Ah, that's a letdown. All right, with that all being said, let's have a look at a genetically modified ultimate Dino Kairis. Oh, this one's the boy. Oh, look at that stripe. Beep, beep, beep, beep. Yum, yum, yum. Oh, there's a skip button for the introduction. Oh, oops. Hold on. There we go. Oh, yeah, that's it. Oh, that's a fighting color. Yeah, look at that guy. All right. Already straight into the battle. Arms raised ready for the hug of death. Will it go first? Of course it does. Of course it does. Even after I kill, the Alasaur doesn't stand a chance against this guy. Look at him.  Go ahead and hit it again. Oh, no. Okay, that'll do. Just one slash. That's all that's needed. This could be the end of the Alasaur. The Dino Kairis won the last battle that I, you know, didn't show, because it was just them hitting each other backwards and forwards. But is this going to be the final hit? Oh, it wasn't burning. It's going to get close to it. Another hit. Oh, two in a row. Oh, imagine if it just leapt on it. Slash, slash, slash. And like, got it into the ground. But I have a feeling that it's either going to end here. Yeah, it's going to end there. But, oh, Alasaur, not looking good now. 4% health. I don't even know how it survived that. All right. Come on, then. Finish it. Is it going to be an interesting animation? Or is it just going to be the slap of death? What do we think, guys? I think slap of death. I don't know. Seeing as how the Alasaur killed it, nothing interesting happened. I have a feeling that Dino Kairis is not going to have anything interesting to do with the Alasaur when it comes to this death. So far, the Dino Kairis gets the Alasaur down to literally no health. 3%, 4%, 7%, and then they just don't know how to finish it. So they both walk away. So that's why this guy has three victories now. And the Alasaur, I think, has three losses? Yeah, three losses. So it will constantly keep losing, but the Dino Kairis is a merciful herb of all and doesn't want to kill it. So that's interesting. So without further ado, let's try something else, shall we? Let's try these tiny little demons. Oh, no, never mind. We didn't do that one. Hold on. There we go. Look at them. Little bats. Oh, hold on. I forgot the thing again. Reptile is the Jalopterus. It's pretty cute, huh?  They'll give you a nasty nip if you're not careful. So be careful. Okay. So they're kind of, they're really interesting looking creatures. They look more like bats than they do Pteranodons. Never heard of this one before. The Jalopterus or Jalopterus. Right. Okay. So I'm assuming it's a fish eater. So we'll give it some fish, but what I always want to do, what's very interesting with Pteranodons is sometimes they have really cool secret animation. So this thing might have an animation where it might interact with a raptor, like a raptor might hunt it down. Or it might have, oh, okay. You're just, you're sat in the middle of the water, please. They're just sort of like, oh, look at them. They're so cute.  What do you need? Insects. Oh.  You don't eat fish. Oh, that must be a new feeder then. Oh, here we go. There's the new zip lines. That's very interesting. Enclosures. Food.  As soon as I placed this down, they came in so quick. Oh, look. So you got moths here. So they sort of land. Oh. Yes. Look at them go. They just go chasing. Oh, what goodies. Let's follow this guy. Where's he going to go? Where's he going to go? Is he chasing one? Nope. Nope. I don't think he's even eating anymore. I think he just ate some and then flew off. Oh, there we go. Oh, yeah, he got it. You know what would be even easier for this? If we did this in slow motion. Yeah. Okay. Now we can really keep an eye on them. Let's see this guy. Oh. Oh, there he goes. Yeah. That one about you. Oh, you're going to go for one? Oh, I mean, kind of seem to appear out of nowhere when you ate that one. So if these guys only eat insects, I'm kind of thinking they're probably not going to eat. What do you call it? Goats. Oh, look at you. A little cutie. Okay. Let's have a look. Oh, you got sonar and skin. Which one looks nice? Oh, that one. Chaplain Valley looks really gorgeous. As well as Quillian Mountain. The contrast with Yukon River looks good too. Amazon Rainforest, that's kind of okay. I think this one's still my favorite so far. And as far as patterns go, yeah, look at that. Okay. That has to be it. The gorgeous. They look like butterflies. Plain. It just doesn't see. Like why would you pick plain when you have all of these amazing, like that one? Oh, it's gorgeous. Okay. So you got Chaplain Valley with Chalcorana. Or even with Quillian Mountains, that looks really cool too. Oh, look at them. Even the ears change and the eyes change as well. Why would you ever have blank? You just wouldn't, right? It's Chalcorana with either Quillian Mountains or Chaplain Valley. It has to be. I'll put a goat in here.  Why not? Yes. Put the goat in. Will the goat die? Run, goat. Run. I'd be very surprised if the goat dies. The goat just drowned itself. And while we're here, why don't we just see what a Quetzalcoatlis makes of them, shall we? Look at this guy. Go. Are you going to hit the fence? Yeah, he does as well. If you do have these guys in your park, you're going to have fair warning before they break out. Oh, did you see that? They just killed the Quetzalcoatlis. Look at them. Cold-blooded eyes of a murderer. Yeah. Oh, it's me. Oh, my God. Someone break out of this already. I'm going to go get something to drink. You better break out by the time I get back. Finally. It only took everything to die to get this thing to break out. Jesus. There you go. Eat some people. Yes. You know what it is? I've never actually just taken control of this. When has a Quetzalcoatlis been around? Oh, look at you. Ah, yum-yum. Shoot the guy. Put him out of his misery. They do move in hits. Okay. No matter how long I wait here. What I tempt them out with. They don't budge. Apparently these guys don't want to leave. Honestly, these things, I don't think you've got anything to worry about. Do you have these in your park? They very rarely break out. And no, they, in fact, no, they didn't. They can't even break out. Even when the gate has been left wide open. Giant hole in the fence. They don't leave. Don't want to leave. Don't you got anything to worry about these guys? Well, hey, you know what? If you guys manage to get some cool animation, Oh, more power to you. But I cannot seem to get anything. So we'll move on to the next dinosaur. Sino-surruptrix. That's it. Look at it. So cute. I mean this one. Yeah, definitely. That's all. Oh, look at all the colorings. That's cool. That's cool. Is there anyone that's not? No. It looks brilliant in no matter what pattern. And then you've got, Oh, okay, okay.  I kind of like that. Also don't mind it when it's just blank as well. But that looks really cool. Yes. Gorgeous colorings. And we've got to see any of the animations. Whoa. Oh my God. It chases its own tail. Oh, that's brilliant. A walking animation. Standing. Sprinting animation. And then the screechy. As well as sleepy time. All right. Well, let's go see this in action. Releasing Cynosoropteryx. Oh, oh, those are, those are really small.  Oh, that's so cute. Wow. Let's see what they have to say about these little guys. Yeah, little tykes. Yeah. You might think that the Cynosoropteryx is the perfect size for a cute plushie to take home. Yes. But this dinosaur is a pretty mean hunter for something so small. Trust me. You really don't want this one snapping at your heels. Ooh. Okay. Okay.  So kind of interested. Oh, have we got a social animation here?  Oh, they scuffle it. Oh, wow. Oh, that's that kind of like little angry honey badges or raccoons. Oh, that's cool. I love it. Now, will they work in a pack a bit like the morocentropides or intrepidissory or the commsognathus? That'll be interesting. I'll release a goat and just see how they do against that thing. Will they choose pith or will they choose death? Oh, at least we got to see it eat from the feeder a little bit. Just get stuck right in there, doesn't it? No, look at your little face. Okay, so it doesn't do what the combi does.  But I'm very curious to see if they're going to put any animation between. Look at these guys. They're rolling in sync. Any animations between other small dinosaurs. Because we now have quite a few. We have listerosaur, this guy, commsognathus, the morocentrepidiss. We've got a lot of dinosaurs that are, they could really have some interesting interactions between each other. Something in the same ballpark. Let's see just what they do. If something maybe just a little bit smaller approaches them. Something like a commsognathus. Around about the same size. Again, a flock animal, especially in this game, where they do a lot of things to goats and people if there's multiples of them. If they're just by themselves, then they don't really. In fact, they look to be maybe a little bit smaller than a compi, which is really weird. Well, I think the compi's a little bit taller. We'll also release a homalosephale as well. Why not? Dinosaur that's a lot bigger. But if there's any herbivore they might have a kill animation with, it's going to be homalosephale. If we're going to get an animation between small dinosaurs, this is it. Otherwise, these guys could all kind of go in one enclosure and be completely fine. Oh, well, you look dead. That's interesting. Oh, these guys do attack in a park. Very interesting. I didn't think they would be.  Squirrels.  Or will the goat survive? Oh, there it goes. Boink. Yeah, it kind of looks like the goat might win this one. And they've just given up. Well, well done. Well done. A plus for effort. Well done. So they are pretty much just like Compsognathus, Mars Intrepidus, when there's a lot of them, they go for the goat and they'll probably also go for people, too. There you go. Kind of broke his neck there. Oh, my God, there's so many of them. The poor goat doesn't stand a chance. Oh, my God, there's loads of them on now. Uh-oh. Uh-oh. Go in trouble or is it? Go win. Go win. Go win. Go win. Well done, goat.  All right. Time for funny games to be over. Let's release them into the public. How? Oh, my God, he went flying. Oh, but no one had taken it on. Oh, it's the wall. Dude, you killed it. She just took it against the wall and died. No, that would die, too. Oh, my God. No. My babies.  And one foul suit. One, two, three, four, five. And then she died. Another copy in the end. Look at him.  Well, I think we've pretty much seen that.   Now on to the main event, the one that we have been waiting for, the Yutoranus. Yes, look at it. There it is. In a Yukon river and Rana pan. But, oh, oh, that's so good. Oh, that is brilliant. A dinosaur that isn't like the T-Rex, you know, that got some feathers and it still kind of looked like a T-Rex. No, this one's been designed specifically in mind for this fur and was one of the few dinosaurs that we kind of know, pretty sure, had feathers. Oh, they're also good. I kind of like the gray one, a bit off-off-white. Where is it? Yeah, that one. Okay, let's have a look at the patterns. Svalbard and Pulcherana. Yep, yep. That's what I like. It's got the Allosaurus or the red crest on the top there as well as that pattern. That looks gorgeous. Although that one looks nice. Kind of like an Aurora Borealis on the head as well as a lovely, beautiful blue eyeball. So Yutaranus actually has five that can hatch from it, which is a lot. That's a lot of big carnivals. And let's see the five animation. Oh, yes. Oh, that looked so cool. Look at them go. Oh, damn. That looked awesome. The feathered tyrant brought so much excitement, so much intrigue to the scientific community when it was first discovered. And here we find ourselves in the presence of that illustrious tyranosaur. Just don't be too surprised if it ruffles a few feathers, okay? I get it, I get it. It was quite interesting about this. It's because they look fairly similar in like pattern to the, especially with this one, to the other small feathered dinosaur, the Soros of something, I cover what it's called. You could technically pretend in your head that these are the adults and then those are the babies because they look very similar, especially when you get the patterns looking like that. And I'm wondering how much of that they thought about actually. Oh, oh, here we go. Oh, a little, okay. I was just gonna say walk into the viewing gallery there, but that was the interaction that they have between themselves, which is quite interesting. Okay, here we go. Here's the social animation. We didn't get a good look at it last time, but this time we should do. Oh, no, it's just trying to be friendly. But can we just talk about that injury animation? How quickly it was like turning its head, like cocking its head to the side as if like looking around as its surroundings and just taking it all in before then, all three of them at the front going out. Ah, that looked so freaking marvelous. That was brilliant. All right then. Right, let's see how you guys eat a homalosephaly. Ha-ha!  You know what? I'll happily take a little battle if that's what you want to do. Go on, go for it. Okay, so yeah. Yeah, I mean, we expected it to be this, didn't we? It was going to be the regular sort of allosaurus battle, medium carnivore just swiping to the right. Like a good old date. Oh, no! No! Oh, bless it, no! Look at the eye on it, like, gotcha! No!  No! Oh, the lift up. Oh, I thought it was going to shake it and go down to the ground, but no. There it is. Oh, there it is! The whack and another one. No! Oh, well. At least I didn't have to suffer too long. Oh, you're not going to eat it whole. I thought you would have. All right then. Look at that. Icy green eye. Murder most foul. Oh, you know, just eating to survive. I'm assuming that they will have the same animation for the goat, but we'll put one in here just to see how that goes. Oh, wow, that did not take long for that kind to go for it. Go on, go! Run! Run, go! Now! Whoa! Oh, what?  Ah, okay. Right. Let's do that again. So the homalicephalate was like a pick up and a bash bash. With the goat, it was just grabbed by the head and just shake it. Bit of a homage to the way the... What was it? The lawyer died in Jurassic Park. All right, here we go. Grubs and then... Oh, shaky shake. Bam, bam, bam. Just break the neck and then... Jesus, eating a goat whole. Just shows how big that goat is when it would slide in there. You might want to chew your food a little bit first, but all right, I'm not one to judge. So we've seen how you snack on something that's incredibly small. But how about a medium size herb before?  You're going to grab it by the neck and do the same sort of thing? Or are we going to... You know what? I'm not going to give you any ideas. You have fun with it. How will this hunt go down? Oh, knocks it over. And then a quick wretch of the neck to break the spine. Beautiful. No, no, no. You're not allowed to eat it. You must take it away. You've got other battles to face. All right. Apparently, Allosaurus just too tough for you to run us. So we'll go with, you know, Carnotaurus. A smaller one. All right. Thank guys. Come on. Go on then. You've already had a little bit of experience. Carnotaurus hasn't. It's really just been born. You've got all the perks, 30% attack, 30% defense, 30% fit, and yes, does go for the first hit. Brilliant. A third bite. Oh, that's it. Third bite does it. Wow. Okay. So no kill animation for the yurt with that. Okay. I thought they could do something quite interesting with it, but apparently not. Well, you know what we got to do? We got to bring in this guy, the friggin' huge T-Rex.  I really don't think so. But let's find it out. That's right. Run. Be afraid. Okay. T-Rex against yurt. It's heads about five times as big. I'd be very surprised if the T-Rex doesn't win this. Oh my God. Didn't even start a jet. Oh. And of course we have to see just, you know, what a Yutoranus does when it's let roam free and it comes into contact with human beings. By the way, can we just look at this carnivore, man? Look at the design of it. It is gorgeous. There we go. Oh, are you on the hunt? You're on the prowl looking for something in the bush, especially this, like the Malta backdrop for it. Looks so friggin' good. Oh. Gorgeous. Oh, she goes. Is she going to get somebody? No. She's going to run to the back of it. All right, here we go. More out in the open. This is it able to hunt down a prey. Oh my God. Jesus. What? What was that all about? That is brutal. Wow. It was like a lunge on the floor and then it like threw. We need to see that again. Heck yeah. Got a good shot of this. Oh, yeah. That looks so cool. And then they're wallop. It's like just a play thing. Where did they even go? There you are. Beautiful. Oh, it just wants to play. Right. Which one will it be? I'm betting the one who's dressed different, but let's have a look. Oh, no. It wasn't. Oh, it suddenly just got caught up and the throw. Beautiful. Well, guys, there you have it. All the dinosaurs saw a scene. Some animation shown. I'm sure there'll be plenty of other ones that we haven't seen just yet. It's very rare. We get a DLC with a dinosaur that doesn't have unique animations. Maybe the go on with the year to run us was the unique animation and a big thank you to Frontier for giving me early access, allowing to showcase all of these new dinosaurs to you guys. And if you've enjoyed the video, leave a like until next time. I'll see you guys later. Oh, bye-bye.


Really happy to introduce our next speaker for this morning, Professor Ram Rajagopal. He's Associate Professor in the Department of Civil and Environmental Engineering. Ram also directs the Stanford Sustainable Systems Lab here at Stanford that focuses on large-scale monitoring, data analytics, and stochastic control for infrastructure networks. Ram has also been involved with many of the BITS and WATS projects, which is one of the initiatives of the Precord Institute for Energy. I'm very happy to have you here, Ram, to talk to us about grid modernization and data science as it relates to it. So the floor is yours. Thank you for the introduction, Arpita. I always loved this program. It's an opportunity to meet all the new students and also for myself to learn from the other faculty in some of the other sessions. So what I wanted to do today is to start with this concept of a learning grid and then illustrate kind of two ways in which we are exploring this in research here at Stanford in my group. And that will help you see how data science can be useful for grid modernization. So what is the learning grid? Just to start off, if you think about the grid as a system, and we will see in a moment details about the system, a system that can be learned or managed from learning is a system where data is collected about the system. Then you apply some learning on that data typically to infer what's the state of that system. Since now I have the states and the goal for my system, so let's say minimize cost while scheduling power, that's a typical objective on the grid. I can take my model and my predictions and use them in an optimization framework. The outputs of those optimization then need to be implemented in the real system. This can happen because we have operators like in our independent system operators, there's actually operators who make the decisions and implement them, or it could happen more automatically like in a storage system where a computer calculates all these numbers and automatically decides all the settings. And then the key is once you take the actions, kind of this system then responds to all these actions, and then you can again collect more data, learn better, optimize act. It's very, very important to have this loop in any of such learning system because what you want to be able to do is by making this system go through different behaviors, you can improve the quality of your model. So in the beginning, your models are not going to be very good, but as you continue in this loop and persist through it, the models will actually typically surpass any kind of traditional modeling which is static. So I had a question there, which is off these four activities, collecting data, learning, optimizing and acting, what is the hardest to do in our electricity grids today? So you can imagine a utility or Tesla or even the Kaizo, the California independent system operator. There you go. So 28% of the people said collecting data, 13% said learning, 40% says acting, 19% says optimizing. This is a very, very good answer here. The two hardest steps are really collecting the data and taking actions. And the reason for this is a reason that goes beyond the technology issue. So typically the data that you need in order to do effective learning involves data from multiple organizations.  And in that case, you need the meter data from your customers, but the meter data is owned by the utility, and it's hard for the customers to share that right away, even though they have the rights to do that. So these institutional barriers and organizational barriers are typically a big issue around data collection. Of course, when we scale the management of our energy system, as I'm going to show in a moment, there will be many, many more data streams and figuring out how to pull all of that together in real time, it's going to become a challenge as well. So there's going to be a technology challenge. In terms of the action, the reason for the challenge here is because in the grid, reliability is the key motivation. Cost comes second. So you'd necessarily want the grid where electricity is available for you whenever you want to use it. If you can do it at a low cost, great. But if not, the priority is the reliability. And because of that, it's hard to implement strategies that incorporate learning. Because typically in the learning, you might end up with models that don't make such good decisions when put into the optimization, and that could affect the reliability. So unlike a web application where if a wrong ad is targeted to you, the consequences of that is not so bad. It's just an annoyance. In the grid, it can have some serious consequences. So typically the way this is dealt with is to take any kind of learning, modeling, and application, or even data science insights from the grid into actions to transform into actions, requires you to do demonstrations at various scales and levels over time. So now let's see why do we need to worry about such an idea of the learning grid. This already happens today, and it's accelerating more and more by automating some of these steps. But why do we want to do that? You must have seen in some of the other talks, you know, what is the grid. So here I just have a summary. The traditional view of the grid is we have a centralized, large generation that produces power. These generators basically produce enough power to meet the demands. So basically generation follows loads. The demands are figured out by the utility who reads meters from customers and goes into a wholesale market and buys the power from the generators. And the system operator makes sure that demand and supply match meeting transmission constraints. And the distribution network is managed by the utility, but just to make sure there's enough reliability so you get the power. There was not much visibility in the customers. And this has worked extremely well for the last 50 to 100 years, if you think about the time of Tesla and Edison. But this grid is changing. We have the addition of renewable storage and so on. So that means generation now is of different types, and it's not necessarily controllable. So it's variable. And at the same time, the consumer is becoming far more sophisticated with a lot of different resources, like, you know, EV charging, storage, the capacity to generate power himself. And there is this idea now that in this grid, in this changing grid, the only way to get a cost effective solution for the balance of power, given the reliability constraints that we have, is not to have generation follow load, but to actually have generation and what we call load, which is everything that's behind the meter, behind the distribution network, meet each other somewhere in the middle. And that opens up a host of questions and issues. And the big challenge is, how do we do that? Because today, we don't necessarily know preferences of people on how they want to charge their cars. We don't really have really good models of the distribution network, which means if I want to have loads participate in matching the demand supply. So I reduce my load when the generation is lower, for example. I need to make sure the reliability of the distribution network remains the same. You know, and even if power is flowing back and things like that. And then in the markets, we are seeing a dramatic change with the participation of all these different resources. And building these solutions and scaling them up, designing marketplaces, algorithms, etc., is the main focus of a lot of the research that goes on. And one critical challenge is how do we get all of the information and inputs needed to design these solutions? And if you look at the requirements of the future grid, there are many different ones. But I feel personally that these are the three critical requirements you're going to face. First of all, we will have to move to a lot more autonomous operations because you're going to have just a larger number of assets and they're interacting in such complex ways. It's going to be very hard for a system operator or a human to then make those decisions as it happens today. The next thing that you need is you're going to have to have far more complex modeling. Today the models are mostly based on physics alone, a little bit of economics to design the markets. But there is no complex modeling. But now if homes are trading power with each other and suddenly they gather together to participate in the wholesale market and generators are making bids, all of this needs to be modeled. And this happens at multiple scales. And it's through novel data because we now have the data through the cloud for all these devices. And we need to be able to use that to build these models. And the last piece, I think, is going to become more and more important. As we are seeing on the grid right now, due to COVID, we need some adaptivity in all of these systems. So I can't create rules of thumb and stick to them. Because, you know, as things are changing, my rules of thumb fail. My traditional forecaster may make a mistake. So now we need a lot more adaptive forecasting. In the same way, if resources are going in and out, depending on the conditions, situations in the real world, we need some adaptation to that as well. So these are the requirements. And the challenge is, okay, how do I address these requirements? Well, the opportunity is really that we don't have to use just the traditional grid data anymore. What is traditional grid data? You will be surprised to learn the traditional grid data are sensors and transformers, some market data, and then some monitoring equipment in the transmission lines. So this is like the transmission network data and the distribution network data. But actually now, we have a lot more information. We have meter data from homes, we have data from the EVs, both while they're charging and while they're driving, there's traffic data, there is all kinds of weather information, etc., satellite data. And to build that learning grid, the real way to scale any solution is figuring out how to combine these different types of data and then doing those learning and optimization steps. It's great to have mathematical models, but unless you can actually tailor them to each one of the individual scenarios, you can't really scale solutions. So where do we use this data in this future grid? And even today, but in the future grid. So I made a little list here, which you can look through the slides. And in my mind, you can group it into a few categories. The first one, I will skip, it's like preparing the data. You all know what that is about. But the real first thing that we want to do is to learn about the different devices, the network, and the dynamics of the system. So there is a lot of that that needs to be done. And it's right now one of the hot topics of research in this area. The next thing you can do is now if I have the ability to learn all the inputs that go into my control optimization, market scheduling, I can also start learning how to do better optimization. So I learned all these inputs. And maybe I also learn about decision making under uncertainty over time. So that learning to optimize in the context of grid with the constraint of resiliency or reliability, that's a little different problem than traditionally what you see in reinforcement learning and so on. So there'll be a lot of research that needs to be done around that. We are starting to see because of climate change particularly and the introduction of a lot of new technologies and the cost curves of these technologies are changing so fast that they are hard to predict. We need to now learn how to plan. Planning was not learned before. We had rules of thumb and we follow them to build a plan in terms of what investments we need in generation and network and so on. That is going to change. And then more in terms of the operations of the system itself, we need to do better predictions of the state. That means to know how power is flowing. Where is it going to? What are the voltages around the network? And those measurements need to be incorporated by all the different systems that operate today. And that has not happened at all yet at the scale that needs to happen. And finally, we will need to improve a lot our ability to detect change both in the short term and in the long term. In the short term are things like, okay, there is a forest fire. Some parts of the grid were taken away. Did it also trip some switches somewhere? And I need to be able to detect that. And of course, even predicting those faults and transformers and so on that might have cost the forest fire. So all of these are things to use the data in. And the last thing I want to do in this introduction and then show you some actual examples is just to maybe give you a tip about if you are interested in this area, there is infinitely many problems. You can go from modeling a storage from data in very high level of detail all the way to modeling the whole grid, all the way to planning for that whole grid over a horizon of 10 years. So those are very, very different scales. But whenever you want to do some data science or data driven research, I noticed a few rules of thumb are very, very useful. First of all, I would start by saying, don't worry about methods. The methods themselves, the exact thing you're going to use a regression, linear regression or neural nets and all that. It's less important because those you will be able to develop it once you have the problem statement set up and all the data that you need. So in fact, the first thing you need to do is you pick the problem you want to solve, you need to identify for that problem, what is the data and typically interesting problems will involve new data. That doesn't mean necessarily new sensors, but it could be sensors that were used for one purpose like a smart meter is used for metering, being used for another purpose, like using the smart meter data to schedule and dispatch storage. That's an example right there. So figure out that and I'll give you a second tip about data. All the interesting data science projects you can go through, you know, journals and presentations, you will notice they will always link multiple data sources together. Even when you're doing projects around reinforcement learning optimization and so on, that also is a good advice. So spending time on this step really, really is key to be able to solve problems. The next thing that you need to figure out is for the problem you're solving, do you have supervision or not? What do I mean by supervision? A lot of times we collect operating data from a system and we don't have labels to tell us what was happening with the system actually. If you have those labels, it is a whole set of problems on how do you build solutions and optimization around it. If you don't have those labels, it's a whole another set of problems. So typically, that will determine the roads you need to take in this. As much as you can, you might find it silly, but labeling data will prove to be your best friend. So a lot of the projects we did in my lab, but also, you know, in Es Azevedo and many others, their students spent time doing this labeling and then from that, it got a lot of benefits. And then the third thing is once you have figured out your data, figured out the question you want to answer, the data, built a little model for it, it's not enough to show that the model performs well. These days, that's not so interesting. We know that you eventually will find a good model. The question is really, can the model give you compelling insights or significant performance gains with somewhat of a practical setting? That's kind of my three tips on how to quickly get into an interesting project. So, you know, using a lot of just traditional data and going for very complex neural network and deep learning and saying, hey, you know, this is this is the innovation. So I'm going to give you a lot of interesting solutions these days.  Okay, so now I want to show you two projects that can illustrate somewhat these issues that that I talked about. First of all, we did a project in the last four months in my group, where we were looking at the impact of COVID-19 restrictions on the grid. So how do the restrictions impact electricity demand? What that was the key driving question, does it reduce electricity demand or does it increase? Because it's not completely clear, you know, you don't consume so much in the office. Maybe you're consuming more at home because your home is less efficient, as an example. The second question we have, when we started this off, we just started with that first question. But over time, we figured out that it's very interesting to understand the relationships between demand and mobility. And one of the reasons we started this project was several of the students were doing lab projects. And for many months, they were not able to access the lab projects. And so we were figuring out maybe they wanted to do something. So they said, you know, maybe we can work on some questions related to COVID. And we had been reading in the news statements about how the load shapes for the grid have changed. But there were many contradictory statements. And we thought, what if we got the data and did a proper statistical study? So that's how this started. And let me show you what did we learn. First of all, what approach did we take? You want to understand how the grid demand is changing, which means you need to know a counterfactual. Because I only observe the change, the demand. But I don't know what would have happened in the absence of change to actually calculate the change. So the first thing that you need to build is what's called a counterfactual model. It's a predictive model that predicts the electricity consumption every day, every week, or every month based on historical data before the incident you're analyzing. And it allows you then to say, you know, if nothing had happened and my predictor is very good, I can believe that this is what would have happened. And you take the difference between those two, that's the change in the demand. So that's what we call the baselining in the demand prediction. And you can then estimate the percent demand change. Once you have that percent demand change, we can then correlate it with many different things. The first thing that we correlated it with was the confinement index. So we have all these different confinement policies around the world. And there is no way to do an analysis unless you codify all those policies. Luckily for us, some groups around the world have actually done the painstaking work of codifying lots of different restriction policies in terms of intensity into some categories. So whenever you have this type of work, that is a necessary piece. And then, you know, you can do things like, you know, understanding the results. So we collected data for 60 countries and regions in the world. This took us about a month to do. We went through different websites and so on, contacted people, etc. And we have now a database of hourly electricity consumption from 2015 to today to all these places. And then some level of corresponding weather data that could be improved. But here's what we got. So first, let's see what happened to the grids. I'm just going to skip that. That was the counterfactual model. You can look later in the slides or read the paper that's in archive. But here's kind of the first result that we got. Here in the colors, the darker colors, you know, the deep red means deeper change. So the demand decreased by 20% versus, you know, yellow means no change. And you can see all the different countries and regions in the world for which we had data. In hashed, it's really about the restriction, the confinement level. So confinement level three was the strictest restriction with the shelter in place. So, you know, people have to stay at home, etc. And we just created the hash so that if in that month, you know, less than 50% of the month was level three, between 50% and most of the month, and then 100% of the month. Since this picture is pretty small, let me just zoom into a few here. Here is Europe and Asia. You can see here that, you know, in the first period, which is February, the first month, not much change. But in China, there was already a demand change. And they were already in level three restrictions. And you can see there that their demand dropped by 20%. And now if you go through the plots here, one after the other, you can see that in some way, the restrictions do seem to correlate with deeper drops in demand. So that was the first kind of insight. But the more surprising insight for us was that, for example, look at this third panel here, if you look at Italy and India, they had a very deep demand change, the same order of magnitude of China. But the other regions did not. And why did that happen? Is that something to do with the intensity of shelter in place or the number of days? Or was it to do with something else? It could be that in some grids, the electricity consumption goes a lot into manufacturing, for example. Unfortunately, we don't have the data broken down through that. And if manufacturing stops, the demand decreases a lot. So that was an important question. And it had nothing to do with number of cases of COVID. So we didn't do a deep analysis on that, but it didn't seem to correlate with that. The other interesting thing is in different regions of the world, with different types of people, different cultures, you could see similar changes in demand. And places with the same culture, for example, the whole of Europe, it's not homogeneous. So there's something interesting going on here. So what we decided to do was, well, since everything is varying everywhere, why not take the demand change time series and use clustering and find out if there is any commonality. And to our surprise, we found four clusters, which we label here extreme, severe, moderate, and mild. You can see here the different countries and regions that belong to each cluster. So the US is mostly moderate. Europe is mostly in the severe category. Just so you understand, in the 2008 recession, electricity demand consumption in the United States in some places decreased by 2%. And it was a big alarm. You can now think 7%. That's gigantic. Or if you look at Italy and India, where it decreased by 26% at its peak, that's a monster amount. I mean, this is unheard of, all of these numbers. So the next question we had is, okay, this is really cool. There's some clusters. So it's not infinitely many ways the demand has changed. And you can see here, there's a decrease in the plateau. We haven't looked at the recovery. That would be the data from June, July, etc. We then wanted to figure out, can the confinement index level actually explain this? In order to do that, we use the machine learning model to regress the demand change against the number of days at each confinement level. And basically, what we found is that the relationship is really stronger with, for those groups where the change was more extreme. I'm going to skip all of that. In terms of load shapes, we found that indeed, weekday load shapes have become like weekends, particularly on regions where the demand change was stronger. So there's a lot of next steps on this analysis, including building models for sectorial impact, using the same methodology to understand climate change impact, and developing a global resiliency monitoring system. Since I will not have time for the second half of the presentation, I'll just open up for questions now. We have a question from Karen. Karen, go ahead and ask your question. Hello. Thank you very much for the insightful presentation. I have a question on your point of having generation and load meet each other. So what is your opinion on variable electricity prices to the end consumer as a mechanism to incentivize end consumers to adapt and shift consumption based on the increasingly volatile electricity production? Yeah. So this is a very excellent question. Right now we have time of use prices, which are not real-time adaptive, even though they call it real-time pricing.  People talk about maybe setting prices monthly. Some customers like Stanford participate in the wholesale market, so they do see the prices and respond to those. The challenge for designing smart pricing is really understanding what is the elasticity in the demand. You need to learn that, because unless you know that, your pricing algorithms might work poorly. The reason for that is also in the grid, all of the dispatches decided that they in advance, maybe 80-90% of the power. So there needs to be some predictive ability in doing all of that. Thank you, Professor. Very fascinating. My question is quite simple. Just in terms of the work being done on grid balancing and grid analytics and grid learning, who are the main actors working on this? Is it startups with utilities and transmission companies as customers? Or is it in-house with utility companies and transmission? Because it is such a whole infrastructure. We're just trying to work out who's actually working. So then, you know, the next project I was going to talk about is called the Global Energy Atlas, where we use satellite data and then more fancy machine learning to actually map all of the grid, the solar panels and so on. And that work is actually being adopted by startups and companies outside of the utility. The utility normally does not have the capability to do all of these advanced analytics. But companies like Siemens who offer products to the utility might want to incorporate such solutions and then they compete against startups that build on these ideas. That's what we are seeing. Whether that's effective or not, that's a separate question. In my view, the biggest challenge is how do you get the data that you need to do stuff. And the two examples I have today, one, we use the public satellite data. I can maybe just quickly show you guys one photo. Here is kind of what we did. We actually mapped all the solar panels across the whole of the United States just from satellite data. And that's really public data. And in the first project also, which we are now transforming into a resiliency estimator, it's also based on public data. So if you're building out of a startup, I think you need to have access. You need to be solutions that leverage public data. If you're in the utility, you have access to your own data, but then you don't have the human capital necessarily that can do all the analysis. And just a related follow up if I may. Presumably these models are system specific. So if you were to go and then apply it in a different country or a different part of the transmission grid, I presume your models would have to be recalibrated or even potentially redone with a different data set. Is that correct? And is that one of the challenges? Yeah, that is a challenge, although creating models today is very easy and fairly quick. So in the COVID example, we actually built 20 plus models and we fit them to every country. And one of those 20 is going to be the best one for each country. And we use that as a counterfactual. For this machine learning here, what we learned is we also have now run this deep solar project in Germany. You do need to retune the algorithms to run in Germany versus the United States, but we managed the whole of the U.S. And then now several states inside Germany and we were able to map. So there is a lot of tuning. I would say that the biggest obstacle, if you're thinking about starting a company on this, is going to be getting the data that you need to answer a question that's useful and who your customer is going to be. If it's the utility, then there's all kinds of reliability questions they're going to ask you, which take time to answer. If you have the time, we'll take one final question from Catherine Burner. Go ahead Catherine. Thank you. My question is pretty similar, so maybe it can be a short answer. So yeah, I was just wondering if there's any other ways to monetize these ancillary services that you're talking about other than directly looking for payment from the utility? Are there any other ways? Yeah, so there is a whole, in the industry right now, there is this idea that you can, if you gather enough resources, you can participate in the wholesale markets. So there are, I mean, that's independent of utilities in some of the system operator plans. In some plans, it depends on the utility. So you can, and there are companies doing that. So here in California, a company that came out of Stanford is HomeConnect, and HomeConnect is they bid into the market and basically send signals to your air conditioning and so on and so forth. The question there is really, how fast can you scale such a solution?  I think the biggest opportunities are around the storage and EV charging and all the analytics that go around that because those resources are much more predictable. And you can have a few large or moderate sized resources, control them and really earn some real money. So we have a project called PowerNet that explored a lot of this, deployed it. And right now we are actually looking for someone who is interested in understanding commercialization to investigate, you know, certain application areas and so on. So if you're interested in that, send me an email. There's a group of five, six faculty that's involved in that project. And that's our next step.


Hey, what's good? Everyone is be Avery here for another reaction. This one is King Arthur Legend of the sword for this being an action adventure drama You would think I would be very excited about this movie, but I'm not I saw the first trailer last year in the summer doing some Doing comic-con and it was cool But I just wasn't that impressed and I just really didn't care to look at the other trailers But this was the third one is directed by a guy richie. Let's go ahead and see what this one is about You felt it The power the legend of the sword of a king are there than yourself Find him you become had you inherited your father's kingdom instead of being raised in a brothel What gave you such drive with this sword? His power is increasing When people fear you it is the most Intoxicating sensation Doesn't it make the palace burn well And I like that a lot better I am I can actually say that I'm actually looking forward to this movie now That was pretty cool. I like that, you know, this is looks like somewhat of an origin story for Arthur a king Arthur You know, he didn't get thrown from his father and he grew up in a brothel or just in the slums like that And he's just gonna have to embrace his true destiny and that's pretty cool. I love stories like that You know, this is directed by guy richie the guy that did the Sherlock Holmes movies one and two I really like both of those and I own them on blu-ray and a lot of people didn't like Sherlock Holmes to the book of shadows I think that's what it's called But I thought that me personally I thought it was just as good as the first one I really don't understand all the complaints that people had with that movie and man of uncle Nobody wants to go see that at the theaters. It didn't perform well, but I really liked that too So, you know for me the three guy reaching movies that I've seen he's three for three And it's just funny that I wasn't excited about this before but you know I am now liked everything that he was doing with the sword and all the sword play and the fencing and the training and What not that same cool?  I don't know Hansel was in there. So yeah, this comes out May 12th You know right around the corner right after Guardians of the Galaxy volumes who comes out So looks like we're gonna have a great summer on our hands and this is another movie that I'm gonna add to my list of being excited for So what did you think of the latest to find a trailer for King Arthur and the legend of sword? Did this trailer turn you on did it turn you off? Do you agree with me or do you disagree with me? Let me know in the comment section below Let's get this conversation going and keep it flowing if you'd like this video Go ahead and give me a thumbs up and if you don't like the video, that's fine Just leave me a comment below why and still give me the thumbs up since you're here on YouTube Go ahead and subscribe to the channel so you can get all the content that I have to provide and also hit me up on my Website social media Facebook Instagram Twitter and all that good stuff But guys, thank you again for tuning in for my opinion slash reaction for King Arthur and the legend of the sword And before you go, don't forget that my name is brain to keep Avery and that's just my opinion peace