Oh, I'm muted. OK. OK. Great, great. So, yeah. So I'm actually not going to be talking about spin liquids. I'll be talking about superconductivity. And I'm going to ask the question, can neural networks capture superconducting order without really any information that the state is going to be superconducting? So we're going to try to start from sort of a bare bones neural network and see if we can stabilize superconductivity of various types. I'm going to be doing show and calculations from variation of Monte Carlo. There's been several really nice introductions to it already, so I'm going to skip that part. But I do want to talk a little bit about my wave function or our wave function onsets, which are these hidden for me on the terminal states. And actually I'm going to add some lattice symmetries to these states. And I'm going to show some benchmarks on the half-filled Huber model. So this is a model that's pretty well understood, actually. And there's some nearly exact results on this predictor system. So it's a great way to benchmark your calculations. And then I'm going to discuss the attractive Huber model, where we're expected to find S wave superconductivity. And in fact, we do stabilize it. And then I will eventually move to the repulsive Huber model, which describes Cooperate superconductors. And show that we do actually see the S wave superconductivity. So let me start with this hidden for me on approach, which is what I'll use. This is an extension of a Slater-Jastrow method. So the idea of Slater-Jastrow method is you have some mean field orbitals for your electrons. So you have one orbital for every site. In this case, we have spin up and spin down. And so you take some determinant over those orbitals. But in this configuration, we actually also have these auxiliary orbitals. And these auxiliary orbitals are output by a neural network. And so what you end up having is your wave function is the determinant of some matrix. The top half of the matrix are these mean field orbitals. And then the bottom half are actually output by a neural network. And so again, as I said before, it's an extension of this Slater-Jastrow approach. And actually, if you expand up the determinant this way as a product of determinants, you can see that actually the first term is just the Slater part of the onsets. And the second term is this Jastrow factor. But you can see this Jastrow factor is actually informed by the mean field configurations. So you have this interaction between your neural network orbitals and your mean field orbitals, which gives you this really, really rich expressibility, expressive power. And so what's great about this method is actually that this mean field part of the wave function can be computed very efficiently for the ratio of this part can be efficiently computed for single fermion hoppings. This is known as a low rank update to the determinant. And so this is very efficient and actually makes it probably more efficient than backflow in some ways, which is a sort of competing method. I will say that I do not, I don't do these low rank updates for the simulations I'm showing. So I won't scale up the huge cluster sizes, but just know that these can be approved a bit. And I can do bigger clusters in the future, I think. So yeah, so why add symmetries to this hidden fermion state? The main idea of adding symmetries in a sort of variational problem is actually you want to take the Hilbert space and you want to divide it up into pieces. So symmetries, what they give you is they give you good quantum numbers. So here we have a Hamiltonian defined in a lattice A. We have rotational symmetry. We have translational symmetry. We have reflection symmetry. And these quantum numbers divide your Hilbert space up into pieces and it makes the sort of search space for the ground state a lot easier. And the other great thing is you can also find low lying excited states that as long as they have different quantum numbers, you can find these excited states. So I'll be showing that this is a bit of a blessing and a curse because you have to try different quantum numbers to find the ground state.  But ultimately, it improves the variational accuracy by enough that I think the trade-off is worth it. So the strategy here is to start with all the symmetries, start as symmetric as possible, and then try breaking some symmetries and see what happens with the wave function. So this is kind of how we add the symmetries to the hidden fermion approach. And this is rather technical and I think I'd rather discuss this mostly offline if anyone's interested. But our neural network orbitals are now this thing called a group convolutional neural network. And we also have this kind of string we have to put, this sign with this pi thing is a fermion sign we have to put into our wave function. And this is because if we define a Hamiltonian in Fox base, basically the ordering of the fermions will break lattice symmetries. And so we kind of have to stick this sign by hand back into the wave function if we have the Fox state. But this is generally how we do it. And I'm happy to discuss more. But I really want to talk about the results because I'm excited about them. And so first I'm going to talk about the square lattice Hubbard model at half filling. So this is the model right here. And this is a paradigmatic model in quantum antibody physics. It's kind of the simplest model you can make of sort of a mod insulator or interactions among electrons. And so basically there's this potential energy term which says that two electrons don't like to share the same site. They interact repulsively. And then there's this kinetic energy term which is the term on the right with the T. And that's just the desired electrons to move around. And so when we do this model, when we study the square lattice Hubbard model at half filling, we can compare with this method known as auxiliary field quantum Monte Carlo. This method doesn't have a sign problem. And so the results are somewhat exact. They're sort of exact to statistical accuracy. And what's kind of great about this also is that there's this particle hole transformation that you can make on the fermion operators. And this actually maps you between the attractive and repulsive Hubbard models. And so we can kind of, even though these two models are the same in some sense at half filling, the ground state looks very, very different in the space of fermions. And so actually if you have a repulsive Hubbard model, the electrons are localized. And you have this anti-ferromagnetic super exchange between the electrons. So they want to be anti-ferromagnetically ordered. So you have this anti-ferromagnetic mod insulator. But when you turn U from positive to negative, you actually get this charge density wave where the electrons are sort of paired at every other site. So even though the model is the same, the physics is very different. And so what we do, what we did actually was we tried both this attractive and this repulsive Hubbard model. And with our neural network approach and variation of Monte Carlo, and we compared our results with AFQMC. And we found that indeed our results are really, really accurate. I mean, on 16 sites, they're nearly exact. And then on 36 sites, they're pretty similar to AFQMC. And so of course, a 36 site problem Hubbard model is the Hilbert space is still way bigger than anything you could do with ED. And we're demonstrating nearly exact results here. The other thing we find though is that it's actually easier to represent this spin density wave, this mod insulator than it is to represent this charge density wave. And so, so actually, yeah, yeah. This is the ground state energy, yeah, no. Yeah, so these are the ground state energies. And on a torus with periodic boundary conditions in both directions. And so everything I show is gonna be on toruses, tori with periodic boundary conditions. So yeah, so anyway, so I just, I also wanted to check to see if we were kind of physically getting the same thing for the attractive and the repulsive Hubbard model. So these are some correlation functions here. So on the left-hand side, we're showing some correlation functions of the attractive Hubbard model. And the right-hand side, we're showing correlation functions of the repulsive Hubbard model. And these correlation functions also transform under this particle hole transformation. And I mean, the main thing to take away from this plot is actually that they look exactly the same. So on the top, we're kind of seeing these charged correlations and we're seeing this sort of, this charged density wave for the U equals minus six. And then we're seeing the spin density wave for U equals plus six. And then similarly for the superconduct, S wave superconducting correlations on the left and what I call spin flip correlations on the right, which is just how they transform under these symmetries. So this is a really good check that actually we're getting the right results in the half field Hubbard model. So I think with this, armed with this knowledge, it's worth it to look at the hold-up models. So first we're gonna look at the hold-up attractive Hubbard model. So actually, and you can see kind of in the previous slide, there's actually this degeneracy between this charge order and this superconducting order. And so actually doping the Hubbard model with holes, this attractive Hubbard model with holes will split the degeneracy between the charge density wave and the superconducting order and it'll favor superconductivity. So we expect to see superconductivity in this model. One kind of nice way to think about it is basically when electrons share a site here, when you create a pair of electrons in a site, they have to form a singlet state. So they're kind of forced to form a singlet state by sharing a site. So when one of them hops away, they kind of stay in this singlet state. And so what you can look for is this idea of off-diagonal long-range order, which is if you kind of create a coop repair at a site and then destroy it infinitely far away, you expect this expectation value to sort of stay finite as you move these coop repairs as far as possible away. And this is associated with sort of breaking a particle number symmetry locally, which is really the hallmark signature of a superconducting state. So we look at these plots of the whole Dove-Tubborn model. We can see that they're blue. So what that means is that the superconducting order parameter you can see is pretty large, first of all, and that it's also persisting all the way across the cluster. And I should say actually that the reason the center is white is just because I set that value to zero so that you can see the scale of the correlation functions. But yeah, so you can see that basically, there's not really, these plots aren't that exciting. They're just kind of, they're blue plots, but it shows that we have this S wave superconducting long-range superconducting order, and it's already kind of stabilized on a 36 site cluster. So we've stabilized S wave order, and now let's talk about D wave order, which is really the more sort of exciting and interesting problem. And so the repulsive Hubborn model is well known to describe the cuprates. The left plot here is sort of a cartoon of the phase diagram of the cuprates. We have these two domes. We have this one, the small dome on the left, which is electron doping, and this sort of larger dome on the right, which is hole doping. And actually the right figure is from a recent paper that kind of combines AFQMC and DMRG, and this paper, they get a lot of things correct. So the two main things they get correct are sort of where the domes are located, the sort of fillings that allow for superconductivity, and then also that the hole doping dome is larger than the particle doping dome. So this is really, really, really exciting, this paper, and it basically shows that the Hubborn model was really, really describing the physics of the cuprates well. Now this paper uses these two methods that are much more established than deep neural networks. They use AFQMC and DMRG, and this paper is often termed the handshake between the two methods, and the idea is that DMRG is, AFQMC is cheaper calculation, but it's uncontrolled and it separates from this Fermion sign problem, whereas DMRG is a more expensive calculation, but it's accurate in some sense, like if you extrapolate the infinite bond dimension. So the idea is to use DMRG to sort of verify that AFQMC is working on smaller clusters, that the correlation functions look the same, that they have the same physics, and then maybe use AFQMC to get the bigger clusters. And so that was kind of the strategy employed by the paper I showed before. The question is like what can neural networks bring to this story? And really the argument for neural networks has been that they can kind of cover these blind spots of DMRG. So DMRG is kind of a quasi 1D algorithm, and it also, the matrix product states always have a finite correlation length, and so if the entanglement scaling is either super area law, like in some gapless spin liquids, or actually which is less discussed is just if the physics is really two dimensional, in the sense that there's kind of long correlation lengths in both dimensions, both of these cases are cases where DMRG might fail, and we might even need neural networks to check DMRG. So we're kind of checking DMRG, which is checking AFQMC in some sense, I think. And the good news is that the NQS results really, really strongly agree with DMRG actually. And I basically have kind of looked at different things, and I haven't really been able to find a stronger discrepancy between NQS and DMRG. And so yeah, first let me just show that we stabilized this D wave superconducting order in the repulsive hover model. So we've added, there's a, the cuprates tend to have this T prime term, which is a next neighbor hopping as well. And so we're kind of looking at typical parameters for the cuprates, and we're doping with about 20% holes. And now I'm defining my pair creation operator differently. So now the pair creation operator acts in a bond, right? So this is kind of two electrons being entangled in a singlet state that live at nearest neighbor sites. And the kind of mechanism which they become entangled by is this, again, this anti-thera magnetic super exchange interaction. And so defining this kind of D wave average pairing function is just averaging over this pairing function for the, you know, adding the X component and subtracting the Y component, because this is sort of the D wave symmetry that's seen in the cuprates. And what we see basically is, again, this plot is blue. And so, although there's still obviously some, some kind of structure in the plot, so it's clear that we haven't quite gone to big enough clusters, but we've gotten to 64 sites. And that's about the limits without doing low-rank updates. So we do see this D wave superconducting order, which is really exciting to see this with neural quantum states and kind of confirms what DMRG and AFQ and CC and also, you know, what's seen in real life. So this is all great news. Yeah, so actually, so what I showed you there was one-fifth whole doping. So I just want to confirm some of the other findings with DMRG, so the other thing you kind of see with the superconductivity is actually these spin stripes. And these spin stripes are kind of thought to have some kind of relation. Again, with this anti-ferromagnetic super exchange that that's really causing the Cooper pairs to form. So to find these spin stripes, actually we have to break a symmetry. So if you look at the left, that's what the actual ground state spin-spin correlation functions look like. And so they're, of course, they have to be symmetric because the ground state's symmetric, but you can kind of break the symmetry at a very, very small energy cost. Which is shown on the right here. And when you break the symmetry, you in fact do see these superconducting stripes and the spins correlation functions. You can see kind of as you go vertically on that right plot that you see this kind of this fluctuation of up and down spins. And actually, so yeah, so my thought about why DMRG is actually working really well on this problem is actually that these stripes, these stripes can kind of form the stripe pattern is a very short wavelength pattern. So even if there's some long wavelength physics that's stabilizing the superconductivity, the short wavelength physics, DMRG can kind of capture, even if the clusters are not completely symmetric. So yeah, so this is what we see with the spin order. And then again, the charge order, we don't see much. Which is what's expected with DMRG. Now, here I'm plotting again the number of, the number of electrons at a site times the number of electrons at different site displaced by some translation, minus the expectation value squared. And so we don't really see much structure in sort of the way the electrons are ordering. So again, there's not really any charge ordering. We see this superconducting order seems to be the kind of strongest thing we see. And yeah, so there's also this one-eighth hold up case. And so actually the green here, I realize I never explained this, but the green here is the charge, the charge sort of the charge. So you can see the charge is pretty evenly distributed for the, or actually it's the whole density of the green. The green's the whole density. So you can see the whole density is pretty evenly distributed for the one-fifth state which I just showed. And then you have this one-eighth state where there's also some superconductivity, but you can look at the charge density and it's actually not, it's not uniform. It looks like there's kind of these ebbs and flows of the charge density. And so first we can look at the superconductivity. And what we find is actually a little strange. The superconductivity kind of only seems to form along one axis here with this, along the short axis. This is a 32 site cluster, so it's actually already, I'm actually, the rotation symmetry is broken here already. And the, but again you see the superconductivity kind of is in line with this, these kind of spin fluctuations and the superconductivity that kind of match up together. And what's going on in the x-direction. So actually if we look at the x-direction, we can look at this sort of charge correlation functions which don't really show much actually. And then what you can do is actually you can just break one of these symmetries. And so once you break a symmetry, the charge is no longer uniformly distributed. If you have the symmetry it has to be sort of uniform on average. But when you break the symmetry you can break that. So when we break the symmetry we actually see these stripes here on the right. And so this again is the charge density wave that DMRG sees. Okay, so yeah that's basically my talk. The, yeah I mean the main thing is that we were able to stabilize these both S wave and D wave superconductivity using neural quantum states. And our findings really back DMRG. And the kind of potential issues of the DMRG seem to be avoided because these spin stripes are kind of formed at the very short period. And so you don't really need a huge two-dimensional cluster to really see the superconductivity in action. Thank you. Thank you, we have time for questions. All right, thanks a nice talk. From the methodological point of view I just have some questions with this, the hidden ansatz and the symmetries with respect to, I mean you made the statement in the beginning that it's more expressive than Slater, just to, I mean of course you can go beyond that with the backflow transformation. Now with backflow transformations the problem is that you can't really describe anything that is very, I mean that is far away from filled shells. So as long as soon as you have some open shells it basically doesn't really, it has a hard time representing those things. So I'm just wondering, you seem to be able to do any kind of doping unless you chose those in a specific way. I don't know how you came up with these numbers but I'm just wondering, you seem to be having no problems with that. So do you think that would be due to the symmetries and the fact that you take into account all the rotational symmetries in your transformations as well or do you think it's rather because of the ansatz that you're choosing of the hidden fermions? Yeah, so I know hidden fermions improved upon backflow by a good bit when it came out but also the symmetries also improved upon hidden fermions. And I mean the symmetries really, really help on small clusters so you know because the Hilbert space, you're cutting up the Hilbert space into pieces which is really, really great when your Hilbert space isn't that that large but then as your Hilbert space gets larger and larger these symmetries kind of become less and less effective in some sense. So I think symmetries are really, really doing a lot of work especially on the small clusters with getting good, good variational energies, yeah. But you didn't look at, for example, the performance as a function of the doping specifically whether this had an effect or? No, I didn't compare symmetries and no symmetries as a function of doping. No, I mean I did compare it a few points like and no symmetries seems to work better. I mean symmetries seem to work better, yeah. Just a curiosity, when you compared with the affilling case in the quantum Monte Carlo for the six by six in order of magnitude of the parameters you used just to have an idea. How many parameters did you have to optimize to get the energy you showed in the six by six? Oh, wow. I don't want the precise number 10,000, 100,000, a million just to know. Yeah, let me just try to get a ballpark estimate and I think 100,000 of that order, yeah. Hi, thanks for the nice talk. You said at the end that here you don't get like so much different physics and energy because the superconductivity shows up already at short scale. So can you imagine any other systems where you might get qualitatively different results with that's not the case? Yeah, so I mean, I think one sort of example would be maybe like a gapless spin liquids, you know, they have long or they really have infinite correlation length in both directions. So that's a place where DMRG should absolutely completely fail and actually DMRG can kind of work if you're like really, really clever and like, you know and like you know the answer already and but but I think it's a place where the MRG fails in terms of superconductivity. Yeah, that's something I'm interested in thinking about that people have answers and the audience, that'd be amazing. I think. Thank you, so let's thank the speaker again.


Hey guys, welcome back to Six Sisters. Today we're sharing with you three pumpkin recipes perfect for the fall time. So I'm Kristen, and I'm Camille, and today you guys liked our other pumpkin video so much that we decided we'd share with you three more pumpkin recipes when they're coming at ya. So our very first recipe we're making is pumpkin sheet cake. So this one is so good because it feeds a ton of people and I love making it for groups or parties or just to eat right out of the can. I even like to make this for Thanksgiving. I'm not a pie lover, but all things pumpkin. Yes. Good. So this is a good one. Okay, let's get cooking. So the first recipe we're making today is pumpkin sheet cake. So let's get going. Let's do our pumpkin first if that's okay. Okay. I just like to put it on the bottom. It just makes my life, just makes my life a little bit easier. Okay. So this whole can? Yep, 15 ounces. And this is pumpkin puree, not pumpkin pie filling. Yes. Okay.  Okay, then we're just gonna add in four eggs. There we go. And then I'll let you add the others while I mix. Okay, so then we're going two, is this two cups of sugar? Two cups, yep. Now it sounds like a lot, but you got a lot of pumpkin in there, so you need some sweetness. And it's for a whole cake. Yes. So let's go. And then oil, we've just got one cup of vegetable oil. Yeah. We'll dump in there. Now you can put this in like your stand mixer, you know, but we're just doing it this way by hand because sometimes it's more fun that way. Right, it'll work out while you are getting ready. So now we've got to add some seasonings to it. We are gonna do three teaspoons of cinnamon. So it seems like a lot, but remember this is a big cake. And then we're also gonna do one teaspoon of pumpkin pie spice. So if you don't have pumpkin pie spice, you can add in a little bit of nutmeg, a little bit of cloves. If you Google how to make your own pumpkin pie spice, it's super easy. Yes. Okay, and then we've got to add in some baking powder, some baking soda. So let's see, baking powder. I've got two teaspoons. Nice. And then baking soda. We've got one teaspoon. And then just a little bit of salt, one teaspoon of salt. Nice. Okay, mix that in. And then our last one is the flour. Hold on. I'll pass it to you. Thank you. Sorry, I'm gonna mix. Okay, so it's just two cups of flour, which means this is gonna be a really moist cake. Which is what I like. Yeah. Almost like fudgy kind of. Like, I don't know how else to describe it. I love like the dense, I don't know, it's just so good. Yes. All right, mix in this in. You almost need a bigger bowl. I probably do. Sorry, I'm getting flour all over you. Totally fine. And the floor. And myself, and the floor, and everything. I get those sides. There we go. That's how you know it's good. Maybe it's a sign I should use a mixer.  Your turn. Okay. Why are you using your muscles? I am going to spray the pan with a non-stick cooking spray. You can butter it and flour it, but this is the lazy way, and I just love using pans. We were talking about before we started filming too, this is such a moist cake with a lot of, it's got the oil and the pumpkin, so it isn't going to be too sticky. So that's why you're okay to use a pan. Yeah. And then dump it in there. I'll dump that, I'll grab a spoon to help. Thank you. Get the edges. You got that okay? Yeah.  We'll do that off camera. Yes. Okay, then I'm just going to spread this around. It's not a very thick layer. No, but it will cook up. Yeah, but it's nice and puffy. I'm just going to try and even it before I start spreading. How's it looking? Perfect. Yep, you ready to bake? Yep, I feel good about it. Okay, now that it's all spread and even, we're going to cook it at 350 degrees for about what, 20, 25 minutes? Yep, 25 to 30 minutes. Okay, perfect, 25 to 30. All right, so came out of the oven. We let it cool for like, I don't know, 15, 20 minutes. You don't want it really, really hot because your frosting will melt. So you want it to cool pretty much all the way. Yeah. So now we're going to make our delicious cream cheese frosting that will go on top, which this is what makes the recipe amazing.  I also love frosting. Yes, she does. Might be my favorite food, yep. But, okay, let's make some frosting. All right, so we're going to start with a cup of butter or two sticks of butter. There are at least room temperature, maybe a little bit softer. Yes. So start with that. Okay, and you have a whole block or eight ounces of cream cheese. It is softened a little bit in there. Okay, there we go. Start mixing that a little bit. Now you don't want it melted because you want it a little bit thicker frosting. So as you are mixing that, I'm going to add in your vanilla. So like what, one teaspoon of vanilla? Two teaspoons. Two teaspoons of vanilla. I love vanilla. Okay, and then as you're mixing that, we'll just slowly add in powdered sugar. So it's about three cups. We'll just slowly add it. Okay, it is really thick. So we're just going to add just a little bit of milk just to thin it out just a little bit. So I don't know, like a fourth of cup or so? Yeah. Two tablespoons and then just kind of play with it, see what you need. Kristen, do you want a beater?  When I cook it myself, I leave a ton of frosting on the beater so I can lick it.  I know, it was my favorite. There we go. Gotta hit the frosting off. All right, she goes and washes those. I'm going to start putting the frosting on. Looks good. Okay. Oh, man. It's all Kristen can do to me. I know, I like this right now. I like it, I like it. No, I'm fine. Okay, we are done with this one. We are going to move on to the next one. Okay, so our next recipe is pumpkin pie milkshakes. And we're going to just make a single serving. You can double this or triple it or quadruple it for as many people as you need to serve. But we'll show you how to make it for one. So we're going to start with a quarter cup of pumpkin puree. Nice, I love this because if you just have a little bit left over, this is a perfect recipe. This is a really good recipe. Exactly, okay. So usually we'll double, like usually it's a third of a cup but because we're just making one, it's like one sixth of a cup of milk. Just a little bit of milk. A little bit. And then we've got a half teaspoon of cinnamon. And then a half teaspoon of pumpkin pie spice. And then we're going to throw in about a cup of vanilla ice cream. So, whoa, whoa, just a couple scoops. Good? Good, okay, okay. Now this is like my favorite thing in the world. This is why I wanted to show you a single serving too because it is just the best little blender and I love it so much and it blends better than all my other blenders. So, and I have a lot of blenders. There we go. So, ninja, ninja single serving. I'll put it down below in the description for you guys if you are interested because it will just blend it awesome. Ready? Here we go. Good enough. Nice. Okay, okay, okay.  Oh my gosh, that's amazing. I know, I love it, right? Wanna try it? Yes. All right. Do you need whipped cream on top? Yes, I do. Are you ready? Yeah, put it on my serious. I will. How is it? It literally tastes like pumpkin pie. I'm ready. I'm ready for Thanksgiving. Cool. Me too. That's like a cold pumpkin pie. So good. Which is the way I prefer it. Me too. Okay, that's awesome. Okay, well this one is done. You guys are gonna love it. Let's move on to the next one. All right, the last recipe we're making is our pumpkin chocolate chip bread. Now our mom makes this bread all the time. I think all year round too. She always makes it in these little small loaf pans and then goes and delivers it to all her friends. So, we thought we'd share this with you because it's the best pumpkin recipe. So, all right, are you ready? We got a lot of ingredients. Let's do this. We got a lot of ingredients, but most of the things that you have. So, here we go. We'll dive in. We'll start with one can of pumpkin puree. So it's a 15 ounce can. Gonna use the whole thing. Nice. While you're dumping that in, I'll dump in the sugars. Okay, so three fourths cup of white sugar, three fourths cup of brown sugar. There you go. Okay. Cream that together. Cream that together. While you're creaming, I'm gonna add your eggs too, so it'll make it a little easier. So, just three eggs here. And then half a cup of oil. While you're mixing, I'll just add it for you. Perfect. So, while you're mixing that up, I'm gonna just add a half a cup of sour cream and I am gonna just kind of eyeball this a little bit. You can measure it. Those of you who love to measure, that's great. Okay. We're gonna continue with our liquid stuff. So we got about two tablespoons of orange juice. I love orange juice. It's kind of a funny thing in this recipe. Right, I thought it was so strange, but the citrus is good. Right, and then we're just gonna add about a half a tablespoon of molasses. And I love molasses. That's the best smell. And we added a little bit more because we knew this was gonna happen. Let's go and stick to the side. Yep. We'll get out about a half tablespoon or so. All right, next we're just gonna do one teaspoon of vanilla. Okay, is that all our liquid stuff? Yep. Okay, looks good. Now we're getting into all the dries. So we have half a teaspoon of nutmeg. Dump that in. Half a teaspoon of ground cloves. Which is the best smell. I know. It's like Christmas blossom. And then just two teaspoons of cinnamon. So then we have one teaspoon of baking soda. One teaspoon of baking powder. And then just a half a teaspoon of salt. Okay, got all the little spices. Done, done. Now the last part is the flour. Yep. We're just gonna dump in three cups of white flour. Good luck mixing. Probably need a bigger bowl. No, this is perfect. Why are you folding? I'll just nibble on the chocolate chips for a minute. So we're gonna add about two cups of chocolate chips. Now I am gonna use a little bit more than two cups. We have another bag here because I love to top it with chocolate, lots of chocolate chips on top because that way every bite you get a lot of chocolate. I'm a purist. I like it plain. What really? I do, I love pumpkin bread with no chocolate chips. I'm a purist, that's what I'm gonna call you now. I'm gonna go with a purist. A purist way. Okay. Oh, it looks good. It's ready. Yes, we're gonna cook it a few different ways. Now I've made this lots of different ways so you can cook it in a loaf pan. It will make two loaf pans. Yes. Or it will make six mini loaf pans. Or it will make 24 kind of big muffins. Yeah, big muffins. Big muffins. So sometimes I like to switch it up a little bit. So we're gonna do a loaf pan and then three mini loaf pans. So you spray it really good because yeah, we don't want it to stick. You could also like grease and flour it if you feel yes, like it, whatever you want to do. Yes, slowly start putting it in. So I'm gonna do this half of the batter. Okay, that's about half. Yeah, what do you think? Yep, perfect. And now these three little ones too. So we're gonna cook these at 350. The little ones are gonna cook for like 40 minutes. Yeah. And not much longer. The big one will cook for 40 minutes. Then you're gonna cover it and cook for another 10 to 15. Just watch it. You just wanna make sure it's not burning. Yeah, but you don't want it jiggly in the middle. I made the muffins yesterday and it only takes about 20 minutes for the muffins to be done. So I'll just take those out and muffins. So they don't burn. So, okay. Let's try stick these in the oven. Yep, let's do it. All right guys, I hope you love these pumpkin recipes. Now if you wanna check out our other three pumpkin recipes, you can find those right over there. Okay, we'll see you next time. Bye.


Welcome back to the IBM Storage Summit. We're here live in our Palo Alto studios. You know the hallmark of any modern cloud company, even one that's say 112 years old like IBM, is its ecosystem. We're going to dig into that ecosystem with Karen Shue, who's the IBM VP of Storage Ecosystems and DJ Singly, who is the CTO and Enterprise Architect at Mapsys. Folks, welcome to theCUBE. Thanks for coming on to our live program. It's good to be here. Hey, Karen, let's start with you. We always talk about your algorithm and what you're trying to solve for. What are you trying to solve for as it relates to the ecosystem for IBM Storage customers? Well, our storage ecosystem is really an extension of IBM. I would say that me, with my ecosystem partners like DJ, we are working to solve the same exact problems that you maybe just heard Scott and Sam talk about. Making from a storage perspective the data available, making it available quickly, making sure it's secure, making sure we have the right governance and enabling our partners to really deliver a complete solution that may or may not include additional products beyond just the storage. So DJ, what's Mapsys all about? What's your sort of unique approach? Yeah, great. I'm glad you asked that question. So Mapsys is a premium or a platinum IBM business partner headquartered here in central Ohio, serving the entire of the United States.  We only sell IBM and we really plant our flag with the IBM Storage portfolio. It's not only about speeds and feeds these days, right? I think the speeds and feeds conversations are over in storage, quite frankly. We're now on what else can the storage do for us? And that's where this whole cyber resiliency and the cyberball strategy to come from with IBM. It's the next level of what storage can do for you and your enterprise. Yeah, I was gonna say that it seems like cyber resilience and being built into the product from the beginning is a key advantage of what and why you go to IBM storage. What are you hearing from customers? Because I think everybody leaned on disaster recovery having been in that industry myself. You start to look at it. It doesn't solve all the problems, especially when you get into this unstructured world. What are you seeing from customers and how is the cyber resilience built in and helping you? Sure, sure. So a couple of things here. So I would say that before COVID, right? There was no such thing as a breach as a ransomware attack in any of our patch. However, since COVID we've had a multitude of customers come down with the COVID cough and unfortunately a little bit of the ransomware attack here and there. And it's a scourge, it really is. And that's where the next part of IBM storage strategy, the cyber resilience comes into play. So MAPSIS unfortunately has had to respond to, I'd say probably eight or nine attacks since COVID started and it always goes the same way. We land our team of engineers on site and they get back to work rearchitecting the environment and bringing the customer back up. And we always again, recommend the cyber vault and cyber resiliency features of IBM storage and other products wrapped around it. It's not just IBM and things like Raktop systems, CyberNAS and other things like perhaps Q-Radar or Splunk, other things like that, right? So IBM is a very big pivotal key of all that but it's certainly not the only product. So once we get the customer back up and running with things like safeguarded flash copy, which is a way to take a logical air gap of your data for use in restores, you really have a much more resilient architecture for the customer. Additionally, we need to save off any of those those encrypted data sets for forensics purposes. And that's what we do. We typically work with law enforcement or other firms at that point to do that, to do that forensic analysis while also getting the customer back up and running fast. Safeguarded flash copy in 2023, that's table stakes. And if you're not doing that type of a security with your storage, then you're just not hitting the mark, I think. Kerry, IBM obviously has a massive portfolio, a lot of different capabilities from product to services, et cetera. How do you think about where you leave off and the ecosystem picks up? What are those dividing lines or maybe you try to make them seamless? But I wonder if you could discuss how people should think about that. So we do really, like I said before, view our ecosystem partners as an extension of IPO. I don't think it makes sense for it to be 100% seamless. Obviously, a lot of things are from a skills perspective, from a product offering solution building perspective, but our partners come in and they add tremendous value. So there is a distinction when you start looking at providing the whole solution, if multiple vendors are needed, if additional skills are needed, if additional services are needed, that's our partners really come in and they come and go above and beyond what IBM is offering to build a more comprehensive solution. So it's both seamless from one perspective, but then also very differentiated when you start looking with the additional value that the partners can bring to our clients. Yeah, I mean, I think, and I wanna circle back around because I think that DJ, you hit on a pretty good point with the customers and what they're doing and how cyber resilience has become since COVID really has to be there. Also though, I'm sure given the economy and what they're seeing, the value of flash and how that cost-benefit value has to play out, what are you seeing there about how they balance cyber resilience versus cost and value? Yeah, yeah, cost is certainly a very big thing really. It really is. But also I think the easy button is something that's vastly overlooked. Other competitive solutions out there, we see them all aren't easy. It's the customer has to install something somewhere, configure something somewhere else, and then learn how to use it. IBM really brings the easy button to the market with their cyber resilience strategy, especially the features that are built into IBM's flash systems. And I'm talking about again, safeguarded flash copy and other features like TPI, two-person authentication, and then lastly ransomware detection features that are now built into the arrays. So these are really important points, right? They're built in, there's very little for you to do other than take the machine out of the box, put it in the rack, turn it on, and start using it. Perhaps you'll need a little bit of a how-to session from your friendly neighborhood business partner. I can recommend a good one, and just to figure out how to use it and after that you're off to the races with all of IBM's latest cyber resiliency protections. So it's pretty great. I wonder if you could both comment on the business case and as to whether or not that's changed. I mean, you've talked about a number of situations or you've alluded to where people got snake bitten with ransomware kind of during COVID or even post COVID. Of course, after you, the horse leaves the barn, you close the door. So the business case has traditionally been, hey, we're going to get hit. We're going to lose money or have a business impact. And it's sort of the probability of that occurrence and the degree of impact. And of course, we know that the probability has increased dramatically. The frequency has increased dramatically. The impact may or may not be concomitant with that, but has the business case changed from sort of fear, uncertainty, and doubt? So you better do this CFO or are there other sort of business value attributes that you can get from cyber resiliency that people should include in their business case? Would you like me to take that? Absolutely. Sure. Okay, yeah. So I think there's a four pronged approach, right? To looking at the business case. There's certainly a lot of fear, I think, out there with a lot of customers going through this, but the cyber vault strategy really plays in four different areas, right? Number one is immutable data copies, which I think it's the easy button, everybody can get there. Number two, it's a proactive monitoring. That's a little harder, but for the most part, we have products like IBM Storage Insights that'll help you monitor your storage and monitor for malware and whatnot in the on the block level storage. Very, very, very good. Other products such as Splunk or IBM Q-Railer can go into play there. Then there's the testing and validation of data, which I would only really say that's more of an enterprise grade type of a solution where we actually take those copies and make sure each one of them is good. And if it's not good, then that's when we have a real hard time with it. We go back and we try to figure out what happened, right? And then lastly, it's the party, it's the rapid recovery. It's the holy grail of why we're doing this in the first place. So I think as a customer looks at this and says, what does my budget look like for storage or cyber resiliency? You can kind of pick the pieces of these four pillars out and say, which ones can I afford? Which ones can I implement now? Or which ones can I implement later? I really feel at this point in time, every single sale that we make, every single one includes the first two, right? So you're going to take advantage of all of those features and functions in the IBM Flash systems. You got to understand one thing too, IBM bundles all this stuff together. It's not like you're going to be nickel and dime for a feature code that you didn't buy because you didn't buy it, right? So it's all there. You should use it day one because why not, right? Especially when you're working with competent business partners that can show you a quick demo of how it's used and how you should use it. And then you're off to the races with it. So I look at it is not really as a CTO or CIO looking at this saying, hey, I don't want to buy this. It's some extra thing. I'm going to get hit. I should rely on my backups. It's more like I bought this new storage drain, this new storage subsystem. It's included with it. I should leverage it because it's going to help me in the future. Karen, thank you for that, DJ. Karen, how unique do you see IBM's approach relative to the competition? And maybe you could give us some details as to why. From a storage perspective, when you look at the products that we bring to market and DJ's been talking a lot about cyber resilience, been talking a lot about Flash system. And that really is our key differentiator. Our ability to recover from a cyber attack in hours is something that nobody else can do. We've got inline data corruption detection that was recently announced and being rolled out and available is something that nobody else can do. So we've got these really key pillars that are there to help our clients be prepared and to identify a breach and attack immediately and then be able to recover very, very quickly. I wonder as well, DJ, one of the concerns that I hear from customers is the insider threat. And so if they want to really get you, they're going to get you. It's like the hit man. He's going to get you. So, but there's many, many things you could do to increase the costs of the bad guys. And that's sort of your goal here is make them go somewhere else that maybe isn't as resilient. So what I'm interested in is, what's the best practice? Do you recommend sort of setting up separate business processes for things that remote vaulting and things like that? Is it sort of, or is that too complicated? How are customers approaching that insider threat? Yeah, I think this again is sort of a multi-tiered approach based on how large the customer is, right? An enterprise customer has certainly different worries than a mid-size or an entry-level customer, right? Enterprise customers are going to have everything scripted. If this happens, do this. And these employees do that. And you have multiple times a year that you'll do a DR test and you'll test all of these features here. The smaller customers again, they typically don't think like that. They need to be agile. They just don't have the time to think like that. So I'd say, you know, they should really lean on the business partners more to help them with the cyber vault features and all these cybersecurity features to help them leverage them appropriately across their enterprise, because they're just not going to have the recovery plans available to them. Additionally, I think the smaller customers or mid-sized customers, when you do have an event, before you do anything, just stop, right?   So, you know, there's other things we can do with insider threats as well with other technologies, which are quite capable, like Edwin Snowden type CIA stuff. And one of our favorite products is to leverage Raktop NAS on a IBM flash top system. That can help with the theft of files and it does a nice job. And we do that often as well. Part of our cyber resiliency assessments and how we help customers when they fall into these traps, so. Yeah, and I think what's interesting is it's not just cyber resilience, right? And I think, Karen, when you look at it, you have ecosystem partners all over the world at this point. And you have data privacy and PII and all kinds of different issues that are really driving some of this technology. How does that work, you know, even outside of the US? And what kinds of demands are you seeing from the ecosystem back to IBM? Well, our partners and our clients both wanna be confident that IBM is able to meet any sort of regulations that are put in place by a local country. So my responsibility is in the Americas, but we work a lot with our counterparts overseas. And so we actually learn a lot here in the US from what's happening over in Europe and other parts of the world. So I would say that, you know, we have to make sure that we're taking all of that into consideration when we're developing our products, when we're bringing things to market, so that our partners and our clients have the confidence and the comfort level that we'll be able to stand up to any of those threats. Yeah, and DJ, what are you seeing from the customers? Because most customers, and especially at the mid-sized to larger, they have to deal with this because they have a European footprint and GDPR or they're in California with CCPA or Virginia with VCPD, I believe is what the acronym is for their Virginia data privacy. But how are you seeing that impacting, because they have to take that into consideration and with cyber resilience. So putting everything in one place and trying to protect it just doesn't work. So what are you seeing from your customers? Yeah, yeah, we do a few things here and we work with some managed service providers that have to go through all of those letters yearly to maintain their certifications. So it's pretty tough. I guess I'll take this down to two, I think easily digestible pieces, right? And the first one is encryption is a really important thing to do and it should be required for every business everywhere. It's practically free on flash systems or any other IBM technologies. And if you're not using it, you really should. There's two ways to manage encryption that I think are very easy. I bring this up because I see even mid-range customers having trouble with it sometimes, right? Number one is you can use a key manager, right? That's important. Those customers that have the skills to do that. But number two, I think was just as important is that you can use a USB key right in the back of the machine to store your encryption keys, right? And that's the easy button because now you don't have to worry about a key manager. Some of these mid-range customers, you just, you know, they don't have the skills in their data center to maintain this key manager over five, six, seven years. You wouldn't leave the phone calls I get after six years of saying, hey, how do I upgrade this key manager? Also, what's the password to it? So there's things like that. But the second part of the answer goes to, you know, with the IBM technology stack, you can take those, you can take your cyber resiliency up into IBM's cloud or other clouds by taking those snapshots of your data and migrating them into AWS or IBM cloud for safe storage, if you will. The kind of cool thing you can do is now that it's up in the cloud, your data can migrate anywhere you want in the world, number one. And number two is you can bring up that data on an IBM flash system software appliance in the cloud anywhere you need to. So that would satisfy some of that GDPR and compliance issues of where the data has to live or where it can't live, right? So for instance, we have customers that are replicated from the United States data centers here in IBM cloud all the way over to Germany, right? And that's fine. Or they need to keep it within IBM cloud and the United States and for various reasons. So there's a solution for anything or any problem that you're looking for with IBM. We've done it. Guys, we're almost out of time, but I have to ask you, I was interviewing somebody that got attacked. They went through a recovery. There was a ransomware attack. And I asked this individual, were you afraid you were going to get fired after this? And he said, absolutely not. And DJ, it was interesting to hear you talk about, don't start pointing fingers, don't accuse. When you think about some of the experiences that you've had with customers, love to, I mean, if you have a favorite one, you don't have to mention them, but things that you learned, not only from the technical standpoint, but the culture and if you could leave us with maybe some advice for the audience. Wow, okay. If I could give you a quick anecdotal story about these customers, right? And then the advice, right? Advice I think is already there a little bit, right? But so the first one, right? We had a large enterprise engineering firm, unfortunately get hits and all of their engineering data was encrypted. That was bad. So we brought in IBM Flash Systems to replace their competitive arrays while we took all the data in the competitive array and handed it over to the FBI, right? We got them back up and running in about a week and that was pretty good for them. They were back manufacturing product by that next weekend. That was good. So we're able to do that pretty fast and pretty efficiently. The second one, second customer, they were using IBM SafeGrad to Flash Copy and they did have an incident and the attackers got in and the very first thing they do is they look for honey pots and backups. A honey bot is always your NAS server. Couldn't get into there for whatever reason, but they destroyed the Veeam backup servers. No more backups, that was pretty tough to watch, right? Not only did they destroy the Veeam backup servers, but they destroyed several parts of some of the VMware clusters. Luckily for them, they didn't hit any of the data. Not all the data was safe. So it was a very easy restore for them for the most part, bring up your VMware cluster again and then get back to backing up all your data, right? So really it wasn't that much of an incident. Also, SafeGrad to Flash Copy and IBM Systems were safeguarding the rest of it. They couldn't get any further than that if they wanted to. This customer has been so excited and successful with SafeGrad to Flash Copy. They're now implementing TPI, the two person integrity I talked about earlier and they're gonna be implementing the IBM ransomware detection on block level, which is really cool technology. And guess what? It's all free, it's all free. That's the cool thing. It comes with the machine. It comes with the latest version of the firmware and they can easily install it. So what was the comment between these two events? Well, the one thing was is instead of panicking, both of them called us. Hey, you're my business partner, I just had this event. Can you help me out? What do I do next? And we walked them through it because we've been there and my big thing for customers is don't overreact when you first get attacked like this. You take a deep breath, call someone who has done this before if you don't have any defined procedures to go about doing this like larger enterprises might, but call someone who's done this before, don't start restoring data because you may restore data from, that still is infected and then 10 hours later you're going to have the same problem, right? And that's going to be bad, really bad. Or you're going to overwrite the data and you can't give it to law enforcement. Now we have no idea or any forensics analysis or trail on what happened to your enterprise and what could happen to others and other enterprises. So my big takeaway is call your best friend, call your business partner. I'm so glad we were able to get those stories in guys. I wish we had more time, but thank you DJ and Karen. It's great to have you on. Yeah, my pleasure. Thank you. All right, keep it right there. More action live from the IBM Storage Summit. We're going to talk about data resilience at the core. There's a premise here that inside the IBM flash systems, there's some IP that gives the company some competitive advantage. We're going to test that. You're up back.