we're going to start with epithelial tissue. And I already told you that epithelium, it's defined by the fact that it is found next to a space. And before we really look at the different kinds of epithelial tissues or like what they do, I want you to think about like, why would there be a special tissue type lining spaces? Where are these spaces that we speak of? Anything that is a space is a space. For example, out here, my fingers are in a space and not me. And my fingers, there's all sorts of spaces everywhere. This space is sort of defining my body. My skin defines my body. And my skin is made, has an outer layer of stratified squamous epithelial tissue. Think about why you need skin to be lined with epithelium. I have a space, rotler, and everything except for my teeth in that space is all epithelial tissue.   What about, we know that that's actually outside the body, what about inside your body? Are there any tubes or spaces inside your body? Dude, your heart, your heart chambers are lined with epithelium, your blood vessels lined with epithelium, every body cavity lined with epithelium, every organ covered with epithelium. It's in every place that you have a space. Why? Why would we need such a tissue? Well, let's look at the functions of epithelium. Think about your skin first. What would be the function of skin? Like, why do we have that stuff? Dude, get rid of it. Nobody needs skin. Just kidding. Dude, skin is all about protection. Epithelium can be protective. Your esophagus is lined with a kind of epithelium, the same kind of epithelium, a little bit different molecularly, as your skin. They serve the same function of protection. Epithelium is also a tissue that is really good at secreting stuff. And if you think about the kinds of structures that secrete glands, secrete stuff. For example, sweat glands in your skin, secrete sweat. And it's epithelial tissue that makes up that sweat gland. And the epithelial cells are actually doing the work of producing the sweat. When you produce, when a cell produces something and then barfs it into the space that it is lining, that's the process of secretion. Epithelial cells are also responsible for absorption. And that's a big job. What kinds of places do you think you would be absorbing stuff? How about digestive system? Absorption is when you take something that is in a tube and you pull it into a different space. Whether you are absorbing it into the body, whether you're absorbing it into the blood, or you're absorbing it into a space that's actually outside the body, absorption is going from the space somewhere else. Now, there's some characteristics, some structural characteristics of epithelium that enable those three functions. So let's look at some characteristics of epithelium.  I'm not going to write it down. And the reason why is because you can't read my handwriting anyway.  Not all of them are shaped this way. Usually, I'm trying to make them kind of regular, whatever. Usually epithelial cells, they're not square. What cells actually are square? No. Or pointy on the edges, not so much. But they're closely packed together. That is a characteristic that you will see of epithelium. The fact is that sometimes it's a single layer of cells that are packed closely together. But other times, there's multiple layers of cells.  Multiple meaning two, because I'm not going to draw this forever. Done. How do you know it's epithelium? Well, dude, you don't unless I tell you this is a space. You see a space? You can pretty much assume that there's going to be epithelium next to it. I'm going to draw another structure that's really important that is associated with epithelial tissues. And it's, I sort of feel unfortunate about my choice of color. This is a structure that is associated with epithelial tissue. I'm going to jot down that it's a structure, just so you know that that's how I would ask you about it. I could point to this structure on an exam or quiz and ask you, name this structure. And I wouldn't say name this cell or I wouldn't say name this thing. Actually, who knows? I might say that actually. Sorry about that. But it is a structure and the best way for me to say it is to ask you to name the structure. Okay, what is this structure? It's called the basement membrane. And it's almost like a placenta between epithelial tissue and the tissue that lies beneath epithelium.  And this is a certain kind of tissue. It's real. That tissue does exist. And I'm going to draw something else going through this one. What do you think this is? It's a blood vessel dogs. Let's play Pictionary. This is a blood vessel. So did I draw any blood vessels going through my epithelial tissue? I did not and that was actually on purpose and it's because epithelium is avascular. There isn't a blood supply for epithelial tissues. Interesting. Epithelium is connected to this underlying tissue by a basement membrane. The basement membrane is produced by the epithelium and the tissue that lies beneath. And what tissue do you think that is? What kind of tissue? I made it messy. There are cells in it. It's like connecting epithelium to something else.  Like think about it. If your skin wasn't, if the epidermis, which is made of a certain kind of epithelium, wasn't attached to the underlying tissue, you just would be like, and your epithelium would slough off. You'd be really disturbing, so don't do that. Let's have a basement membrane and let's have connective tissue underneath. Varying types of connective tissue are associated with varying types of epithelium to make different structures in your body. The basement membrane is produced by both the epithelial tissue layer and the connective tissue. They work together. That's kind of why I was envisioning it like a placenta because both parties are producing this structure. It's a protein-ish structure and the purpose of it is to sort of anchor the epithelium onto that connective tissue layer. It's also a place where materials pass through the basement membrane in both directions. If we're feeding the epithelial tissues from the blood, isn't that the whole purpose of blood? Yes, pick up garbage and deliver nutrients and goodies. If there is no blood supply in the epithelium, then somehow the good stuff has to diffuse through the basement membrane and into those epithelial cells because they're going to die. If this epithelium was in the digestive system, you can imagine that nutrients and stuff that you are eating needs to be absorbed into the blood to be delivered to the rest of your body. And so the basement membrane is sort of a wall that stuff has to go through. Well, look at my connective tissue. Connective tissue is a mess. Connective tissue connects things and surrounds things and basically is like packing peanuts and that's awesome. But epithelium is directional. There's a space edge and there's a basement membrane edge, right? And every epithelial tissue has this. So we actually have directional terms to describe epithelial tissues. You have the luminal or apical edge of a cell or a layer of tissue and you also have the basal or basolateral layer or direction edge of a tissue. My epithelial tissue, this whole thing is my epithelial tissue. And so the luminal edge of my epithelium touches the space and the basal edge of my epithelium touches the basement membrane. You can always have, well, just like all directional terms are relative, B point B is luminal to point C but basal to point A. So these directional terms are relative. Okay. The last thing that I haven't, I'm going to have to write it down because I don't have a way of drawing this, although it could be interesting to try. Epithelial tissues are highly regenerative. What's that mean? They replace the cells, replace themselves quickly. In fact, I want to say that the epithelium lining, your digestive tract, replaces itself completely every 12 days. So the entire lining completely regenerates itself every 12 days. That's fast, like that. Your skin does not regenerate itself every 12 days. Why? Why would epithelium be so quick to replace itself? Well, think about it, dude, you're lining a space, you are basically the border to the outside constantly and stuff is constantly having to cross or slough by you. Think about your digestive tract, man.  Acids and enzymes, like your cells are taking a beating in the digestive tract and so they got to heal themselves. They got to handle that. So those are some characteristics of epithelia. And now let's talk about how we classify these things.


Hello? Anybody here? Are you okay? Wait here. I'll check it out. I'll check it out. Wait here. I'll check it out. Wait here. I'll check it out.


The floor is littered with cat litter just about everywhere upstairs and downstairs. We have a cat that I guess has died and been f***ed and f***ed on all over. Do you ever wonder what happens when the police leave? Crime scene cleaners are private companies that handle the cleanup after the police are gone. Spalling Decon is one of the nation's largest cleanup companies handling the aftermath of homicides, decompositions, hoarding and much more. These are our stories. Morning, I'm Robert and today I'm with Spaulding Decon and today we're going to be taking care of a house that has a little bit of a cat problem. As you can see, the floor is littered with cat litter just about everywhere upstairs and downstairs. Now, we're taking care of this individual here. Her health is particularly felt and declined. So we're here today trying to assist her in getting this taken care of because obviously she's not able to do that. So we have to come down here and provide our services to her, make sure she's in a clean, healthy living environment. That way we can see her living better and in a more healthier situation pretty much. Right now where it's at, it's pretty much uninhabitable and I think anybody would agree with me looking around. There's just about cat litter everywhere. Like in every crevice you could find. So as you can see, it's pretty much littered everywhere.  But I want to take you upstairs so you can get another glimpse. So she and her bad health is actually unable to make it up the stairs I believe. So up here is pretty much not being taken care of. The cats pretty much took over. The entire bathroom is in decline. So there's even cat feces in the bathtub. I'll let you come over here so you can see it. So even to the extent of the bathtub, the felines had nowhere else to go. They found the bathtub and they made it another spot. So as you can see, when something isn't taken care of for a proportionate amount of time, this is what could happen to your residence, apartment, house, pretty much anything. So with these animals that require so much love and attention, this is heartbreaking to see. So obviously we're here to fix it and I think by the end of this you'll definitely see a change, that's for sure. So right here we have a piece of carpet that's been untouched for several years. The rest of the carpet is pretty tattered at this point. You could tell that there's this rough stains and it's very coarse. You could tell the texture of it is just very different compared to this side It's been very exposed to urine, water, all kinds of different particles and dirt elements. And that's what makes the carpet over time turn into this. Essentially when you first get a carpet, this is what it's like. You see how it pulls apart? This over time is the end result and hopefully we can get it fixed and better for them. That's what we're here to do. So today we're going to begin the process of removing this carpet. Basically that entails us getting everything off of the carpet first. All the clutter, all the debris, anything that might be in the way like this animal feces for example. So this carpet here is tainted beyond repair so you couldn't have it steam cleaned. The way it looks now is very destroyed and distraught so we're going to rip it out and we're going to roll it up and we're going to take it out. It looks like it's been here for maybe a year, months at least. It's been here for a very long time and we're here to take it out. So we're going to actually get this whole room cleaned up to the point where we can roll this carpet out, cut it out. Our job is going to be to take care of this and knock it out, get it out of here. Hopefully you'll see a better difference in the end because it's definitely not going to be like this. Have you ever wanted to become a crime scene cleaner like me? Well now you can. Spaulding Decon has put together a variety of courses that can teach you everything you need to know about becoming a crime scene cleaner. We will explain everything on how to get crime scene cleaning jobs, how to clean up biohazards and hoards. Click the link in our bio and use promo code CLEAN for 10% off. Now back to the video. You can see there's plenty of dust wrapped around each one. So you know it's been there for a fairly decent amount of time. I mean the cats love this place. Something that cats love to do is cats love to crawl up under your bed. That's one of their favorite spots. If you have a household cat, that's always what they're going to do. They find it safe under a mattress. So as you can tell, this place was neglected for a while. We've been working on the floors up here. We just reached under the mattress up here in this bedroom and we realized that this was also one of their favorite spots as well. Now as you can tell, just like where the litter box in the corner was, this area's highly been used for feces and urine. So the old birthday card that's in here, saturated in urine. So as we can see right here, we have a large pile of cat feces right here on the floor. So what we've just been doing all day is we've been going around here, ripping out the carpets, collecting any sort of trash material or hazardous, you know. Just anything that can be contaminated with feces or urine in it really. So this rug right here was probably one of the more serious rugs I think in the house, but besides the upstairs bedroom, there's a large pile up there. Okay guys, so we're at the end of day one here at the cat house. And I just want to give you guys an inside view of what's going on and how it looks after we've spent a whole day pretty much decontaminating the residents. And you can kind of have a view for yourself and see like the process of how it works.  So right here in the main foyer, this is where like there was plenty of clothes, junk and trash right here on the floor. So the first thing we did when we got here was we organized and secured all of the loose garbage. Anything that had cat urine on it, cat feces, anything like that. Because you know, that was the main priority was getting that out of the house. It was a health hazard, right? That's what it looked like after we pulled everything off of the floor. Because remember I said there was clothes on the floor and garbage and stuff like that. So what we do is we basically remove this layer of carpet and down to the foam pad here. Once we remove that, get it all cleaned up. So as you can see throughout the house, it's mostly just concrete on the floors because we removed most of the carpet already. When you have the carpet, most people don't know, but the carpet actually has a foam pad underneath of it. So this foam pad is what makes the carpet feel soft, right? The soft material is just the fabric for the top. This is what makes your carpet cushion. This corner was littered with so much cat feces. It looked like the mountain of poop and Jurassic Park. It was huge, right? It looks very empty. It looks like it's under construction, right? This room had a bed in it, furniture in it. That's the only piece of remaining furniture left in this bedroom. But this room wasn't actually as bad as the rest of the house, but just to kind of give you a glimpse of what is going on inside the house and how we're operating. At this point, we've got all the floor of the carpet pulled up to where we're going to come in. We're going to disinfect everything. Tomorrow will be day two. We'll come in here, disinfect everything, sweep it all up, get everything, clean the walls. So I think that just about wraps it up for this house here. And I really hope you join us again on our next video. So good morning. So we are here at a hoarder house of cats. The lady had probably 20, 30 cats in her house locked up with her. So it is a major job for these technicians today. They are going to have to remove everything from the house and also clean it out. Faceboards, tax strips, carpet, you name it, furniture, anything that you can, because you will not be able to get that cat smell out if you do not remove it. So they are here today to do that. As you can see, they're halfway filled a dump trailer already. And from there, we'll see how it goes by the end of the day. Yeah, those are definitely palmetto bugs because they got wings flattened. Look, he still got his whiskers and some teeth. Look at those. Look, it still got fur. It didn't die too long ago. Yeah, so I was cleaning out the garage of this lady's house. And well, we found two cats that she said that was missing. Obviously, they didn't make it. Actually, they looked like they didn't really die that too long ago because probably because of the heat and stuff. They mummify, but they have getting roaches and other bugs or building houses. Like, look at this one. This one still has the hair. And look, it looked like it was meowing when it died. So it's probably in a lot of pain. But I found them squished under a bunch of stuff. Who do I make of today? I make of today the fact that this lady is very sick and needs a lot of help. To have this many cats in a house with her to leave a mess like this, I would rather clean a decomp any day of the week compared to this.  This is my buddy from Joe's apartment. Remember that show? Today we took care of the garage and as you can see from the first day we had a lot of work to side with. There was basically just garbage everywhere, chat bts, cat urine. This was not as bad as the rest of the house but I will say that it has fair share of critters, bugs, all kinds of nasty stuff is laying around. We basically started just by removing the trash after that. It's all basically just clearing it out until we can make room and throw everything away. So we have to move this bookshelf because from the rest of the carpet being contaminated like it was, this part of the bookshelf is actually exposed under the bookshelf to where the carpet shows. So our job right now is to move this giant shelf and we're going to move it and rip out the carpet. We're finishing up here at the cat hoard house. We got this call through code enforcement. We had a call to remove feces and urine and then after that we did disinfect the walls and the floors under where the carpets were. In order to get to all of these items of course we had to throw out the whole. It took about two days to complete with four people and that's another job well done. Hope to see you in the next video.


OK, thank you very much for inviting me to give a talk here. Snezhana, special thanks. Today I'm going to talk about anomalous super diffusive random walk. We call it levy walk. And the key word is anomalous. First, let me give you a brief outline of my today talk. At the beginning, I will remind you the basic idea behind levy flight and levy walk. This is just a reminder. And then I will concentrate on levy walk because levy flight is not a good model for super diffusion. So that's why I concentrate on levy walk. And my purpose today is to present a new equation for levy walk, so-called single inter-differential wave equation for levy walk. The next part of my talk involves anomalous transport on network. And I will discuss scale-free network, metapopulation transport. And the key thing today is axiom of cumulative inertia, which leads to so-called anomalous aggregation on network. Great. So it's a widespread phenomenon in nature, in biology, in physics. We have the following random walk. So the particle moves straight line, and then turn isotropically. Then again, move straight line, turn. So we have a specific random movement. And the specific things about this random movement that this length path, path length L, is distributed according to Paolo. And what is interesting is that alpha varies from 0 to 2, which means the second moment is divergent. And this is everywhere inside cell, the particle, in turbulence moves that way. Sometimes the human travel, animal foraging, especially bacteria moves in that way. What is the problem? The main problem is because second moment is divergent, we can't define diffusion coefficients anymore. So standard diffusion equation doesn't work. Again, we can't write down Focke-Plunk equation. And that's why we call it anomalous transport. And two basic models for this kind of movement is Levy-Flight and Levy-Wolk, with heavy tail probability distribution. Now, what is the difference between Levy-Flight and Levy-Wolk? Because of application in biology nowadays, biologists change words quite easy. For them, it's just the same things. Levy-Flight, Levy-Wolk, and a lot of publication nowadays. But what I'm going to tell you that Levy-Flight is an example of Markovian process, easy and very unrealistic. Levy-Wolk is more realistic, but of course, more complicated. So Levy-Flight. Levy-Flight actually involves instantaneous jump. So if we are here, we start from here, then we just jump with infinite velocity at this position. As a result, we have Markovian process. And if we consider long time limit for this kind of random walk, write down master equation, consider proba, large time scale, we end up with this very nice equation. So instead of Laplacian, we have a fractional Laplacian. And clearly, it's Markovian model. But the problem is that for Levy-Flight, if we consider the position of the particle at the time t, square it, and find the expectation, the answer is infinity. So it's really unrealistic model. It's beautiful mathematically. That's why it's used all the time to describe this phenomenon with length path with a power law distribution. And of course, mathematically, it's nice because this equation has a very nice alpha stable distribution. In fact, when we can't define diffusion, so central limit theorem breaks down, now we have so-called generalized central limit theorem. And that equation provides this stable distribution solution. But again, because of this property, it's really unrealistic. Now we have to turn to Levy-Volk. Levy-Volk involves a random movement with finite velocity. So if we start from here, we just smooth the long straight line with a constant velocity. Of course, it's more realistic. But the problem is that the whole process become non-Markovian. Because of non-Markovian nature, of course, this fractional Laplacian is not enough. And the question is, what is the governing integral differential equations for the PDF of the position of the particle at the point x at the time t? So until now, that equation was not available. So usually, random-Volk or Levy-Volk formulated in terms of two integral equations. But the differential equation for PDF was not available. And that's the purpose of my talk, to present this new equation for Levy-Volk. Basically, there is a two approach for Levy-Volk. One is based on continuous-time random-Volk. The main idea in terms of random-Volk theory is to introduce joint probability density function for running times and lengths of displacement. The idea is almost trivial. If we have finite velocity, of course, the length, the particle moves along straight line is trivial as speed and time. This time is random. Of course, this is also random. And in order to create a heavy-tailed distribution for the length, we just need to create heavy-tailed distribution for running time. So this is psi. And then we can formulate the whole problem in terms of continuous-time random-Volk. But there is another alternative approach. That's what we use now. It's a structural density approach. Since Levy-Volk is non-Markovian, we can introduce extra running time. And then the model becomes Markovian. This is standard precision stochastic process when we extend phase space and makes non-Markovian process Markovian. That's what we did. And we introduce a structural density. So this particle moving right and left, the position x is the time t. And then we introduce extra running time. And then we introduce the rate of change. So if the particle moves, there is always probability that the particle changed direction. And now the rate of change depends on running time. No, no, now I'm going to discuss 1D. This is just illustration. But it can be extended very easily for 2D. The key things. So the rate at which the particle changed direction depends on running time. And it's inversely proportional to the tau. Because for levifolk, we have a different behavior. If we consider the position of the particle at the time t, squared it, and take expectation, then it's proportional to the t to the power gamma, where gamma greater than 1 and less than 2. So this is an example of anomalous persistence. So if I move in one direction, then I keep this direction. And you see, the longer I stay in one direction, less likely I change this direction. And this creates anomalous persistence. So you see, gamma is greater than 1. So it's faster than diffusion. But at the same time, because gamma less than 2, it's a ballistic movement. So it's still random, greater than diffusion, faster than diffusion, but less than ballistic movement. How to create this super persistent movement? That's a very simple assumption. So the longer I stay in one direction, less likely I change that direction. And we consider the rate instead of joining PDF for many reasons. Of course, in linear case, it leads to the same equation. But if we explicitly introduce running time and the motion becomes Markovian, we can take into account a lot of things. And if we have a lot of particles, moving crack, levee walk, we can take into account proliferation. This is important for microorganism, bacteria, cancer cells. Also, we can change the rate. That can be nonlinear. So if the rate of change depends on running time, this is standard linear model. But if we have a lot of particles moving in one direction, it might change the rate of change of direction. So the point is that structural density approach allows us to take into account a lot of different things, including nonlinear effect. And no consistent methodology to include this effect if we start from standard levee walk approach involving PDF for the jump and running time. Right. Now, if lambda is constant, I hope everybody knows, this is a classical persistent random walk model. So in order to extend trivial diffusion equation, it was happened in the past by Taylor, by Kaz, but many other people, they introduced finite speed of propagation. So the particle moves with a constant velocity and then change direction. Moves in another direction and then change direction. If the rate of constant, then we all know that it leads to the classical telegraph equation. So that's a classical telegraph equation. And of course, when lambda, the rate of change tends to infinity. And velocity tends to infinity in a such way that the ratio is a constant. We can introduce diffusion coefficient. So central limit theorem works. And of course, this is just a trivial extension of classical diffusion equation. But you see lambda is a constant, the rate of change. What happened? From a probabilistic point of view, what does it mean? It means if the particle moves in this direction with a constant velocity, the running time is exponentially distributed. As a result, we have trivial Markovian model. But what happened if the running time is power law distributed in a such way that second moment is divergent or first moment is divergent? What happened? What kind of equation we might have in this case? And that what I said at the beginning, that equation was not available. So everybody knows telegraph equation. It's in books, in everywhere. But if the running time is generally distributed, extension of classical telegraph equation was not available. So I'm not going to give you technical details. I just give you final equation because I don't have time. So equation looks like this. So extension. Of course, we still have standard wave operator because we have finite speed of propagation. But instead of this simple term of relating to friction, we have a very complicated operator involving the memory kernel. And memory kernel can be defined through the Laplace transform of the running time distribution. And again, I'm not going to give you details. If you're interested, then this is a paper in which that equation solved in particular case. Of course, we have that property of super diffusion. And recently, we extended this equation for several dimension. And also, we managed to take into account proliferation of random walkers. This is just one example, a very recent example. Just imagine we have many particles. If particle moves in this direction, but then I've got around you a lot of other particles moving in the same direction. What do you think would be implication for the particle? How the rate of change of direction change? I guess if we have a lot of particles moving in one direction, let's take into account public opinion. If you're all your friends moving one direction in terms of particular opinion, what happened to you? It's very unlikely you change your opinion. You just follow others. This is very well known psychological effect. So the same for particle. If the particle moves in one direction, then the rate of change drastically change. And that's what we're taking into account. Also locally and known locally. As a result, we managed to find non-linear equation which generates levee walk. So it's not just postulated from the very beginning. We have power law running time. No, we don't have that. As long as we have a lot of particles moving in one direction, that might generate this ballistic, no, sorry, subbalistic super diffusive movement. And that's what I've done recently. So this is a new paper in which we managed to show that due to non-linear effect, we have emergence of levee walk as a result of dynamics. And this is just illustration of the structural approach. Right. OK, now I'm going to turn to transport on a network. Well, we can see the transport of particles on network. And this is classical scale-free network in which the node with k links had that probability. So it's a power law distribution for the number of connection. So if we have nodes, then, of course, every node got some connection, let's say, k connection. And that connection distributed according to power law. And this is well-known, of course, network. We know the dynamical mechanism, which leads to this scale-free network due to preferential attachment. Now, let us consider the transport or levee walk. Sorry, no, just random walk on network. We have a lot of particles that stays particular node, then jump to another node, another node. In general, it's quite a complicated problem. But if we consider everything in terms of mean transport equation, then this is the key. If we introduce the mean number of particles in a node of order k, let's say here, we have that amount of particles. Of course, it's a function of time. And we can write down a simple balance equation, mean field transport equation. So the particle escape from the node and coming from different nodes. And this is a very trivial equation. And it has a stationary solution. And this is really important. So can I have your attention, please? So in long time limit, the number of particles at particular nodes proportional to the k, the number of connection. So the aggregation of particles happens in the nodes with a lot of connection. So that's why we say it's good to be well connected. And well-connected nodes are more popular. So this is a very well-known result in network theory. Of course, it's many things based on this idea, like page ranking and many other things. But the standard assumption is that the escape rate is Poisson process. So they just introduce the rate of escape, lambda. And of course, if you want to understand what is the rate at which particle escape from particular node like this, this is just a product of lambda and the number of particles in this node. But it's well-known the human activity is not Poissonian. So now, again, I'm going to use the same law. If you are in some position, what is the rate of escape? For human, it's not constant. I'll give you an example from our academic life. If we have postdocs, they move from one university to another. And the rate is usually constant. But what about professors? As long as you find a permanent position, you're trapped. The longer you stay in your position, less likely you escape. I just look at you. How long you spent in your permanent position? For ages, 10 years? I'm personally, I'm trapped in Manchester. Already, I didn't expect. When I turned up in Manchester 20 years ago, I didn't expect I would stay. But I'm trapped. Why? Of course, there are a lot of reasons. Of course, promotion, kids, mortgage. But the whole idea is the longer you stay in a position, less likely you escape from that position. And it doesn't matter. Residence time, employment, state of mind.  The same for conservative. So this is quite universal law which we explore. And that law called axiom of cumulative inertia. So the individuals escape probability from a node decreases with the residence time spent in this node. This is a well-established fact. It's just the same. If I move in one direction, the longer I move in this direction, less likely I change the direction. The same things if I stay in the particular point, the longer I stay, less likely I escape. Of course, mathematically it can be formulated like escape rate depends on residence time, and it's inversely proportional. Of course, this rate, it's very easy to show, leads to the power law distribution. And the question is now, let's say we have a node with a lot of connection. And now we have another node, let's say with two connection. But the escape rate obeys this law. What happened? That's a key question we put in this paper. So standard theory tells us the aggregation happens here because of a lot of connection. Now we have a competition with a node with a lot of connection, but with a standard Poisson law of escape, and then with anomalous behavior. And of course, you can expect what we showed that this anomalous node in long time limit completely dominates the process. So it's not important the number of connection. It's important how much time you spend on that particular node. And if the residence time is power law distributed with infinite first moment, that would be dominant node. That's what we observe in marketing. You see, let's imagine the street with a lot of shops. You're doing shopping, get in, get out. But then if the manager managed to attract you and trapped you in a shop, and you spent a lot of time with the power law distribution, you spent a lot of money in that shop. Because it's a nice correlation, nice data between the time you spent in particular shop and the money you spent. So it's very important to be. And again, the whole idea, as I mentioned, what about attractiveness of the place? What this means? Bad university or good university? In a good university, you are trapped because of different reasons, of course. But this is just universal law. And that's why my PhD student, Helena Stage, they've got a lot of review. They ask the member of staff in our university how much you already employed in this university. And this very unusual behavior, that's what I'm going to discuss right now. So main result is, but what about main result? How to formulate this mathematically? We managed to translate this axiom of cumulative inertia, which is famous law in social science, into a very nice mathematics. So that's the difference in terms of mathematics. So for the classical escape rate, we had this law, which is a standard first order reaction. Now, if we apply axiom of cumulative inertia, instead of constant lambda, we have a operator. And that's Riemann-Lübild fractional derivative. So this law of cumulative inertia leads to this fractional derivative. So fractional derivative is not just postulated from the very beginning. It just derived. And of course, this fractional derivative generate this phenomenon, anomalous aggregation at the node with this behavior corresponding to axiom of cumulative inertia. Again, this is the result I already described. Now, what happened? So if all particles, at the end, aggregate in this particular node, of course, how to prove this is real phenomenon, then that's what we did. We introduced a structural density. It's a function of time and also the residence time. And when time tends to infinity, this has a very beautiful structure. And that's what is here. It's not clear if you look at how it looks like. But in terms of picture, it looks like U-shaped distribution. So we found the structural density in this anomalous node behaves like this. And this is ratio of residence time over total time of observation. So in terms of academic life, it's a very simple interpretation. If you look at the member of staff in a good university, their eyes are all professors on the right and young assistant professors. I'm not saying nothing is between, but that will be a distribution in a good place. That's what we are going to do. My PhD students, of course, ask the Human Resource Department and Oxford and Cambridge they refuse to provide the data so far. But again, we strongly believe that would be because from my particular experience, as long as I speak to professors, they always stay in one place, usually. Of course, some staff professors move around. But most of the people, as long as they're in a good position, they stay for 10, 20 years. Because of different reasons, of course. About, oh, no, no, that's what I put in this category, post-docs. Yes, yeah. But again, this is like a joke. But in fact, it's not a joke. In the past, I described this as like a joke. But then when we got this anomalous aggregation result, again, I asked my brilliant PhD students to find the data. And that was not easy. And she managed to find data in American Midwest. There's a statistics available of residence time in some particular five minutes. And then she managed to find that this is your shape distribution. So if you found a good street in a good place and do a survey, I am sure you will get that result. Because either residence from the very beginning or newcomers. And of course, something in between. So to finish, of course, everything is here related to non-Markovian process. It's not easy to deal with. And again, it involves fractional derivative. And those fractional derivatives, in terms of time, is not just again postulated. We managed to translate this social law into this fractional derivative. Now we have an extension of that model. But generally speaking, of course, description of non-Markovian reaction transverse in network is not available. It's just at the very beginning. And in this book, we can find the theory on non-Markovian process. OK, thank you very much.


hell yeah what's up guys it's curse pike my friends call me big C back in action today we're back on mgn and we're going to be talking about game reviews in my last video i suggested that you guys maybe if you're a game reviewer a video maker or a writer or both you should reach out to me let's get you promoted let's get you some more views some more subscribers and let's get you another revenue source well here it is guys just the other day on may 6 to be exact my buddy armani aka sunstrider submitted a review this is it right here triangle strategy spoiler free review on may 6 he submitted it to us on may the 7th it was ranked number one on google for the search engine results and what am i talking about let me show you here if you type in triangle strategy spoiler free review it's a long term but it's a term and it's an important one you'll see here at the top you're going to see a whole bunch of videos which of course if you guys are working with mgn those could be your videos as well and then in the search engine results for written content mgn is number one there we go it's right here we're above reddit we're above twitter we're above all of those things nintendo life we literally have the number one spot for that search engine term right there and if you want to check into it you can always just go here and launch a new incognito window so there's nobody signed in and know nothing and then just do it you know what do you want to type in what was it triangle strategy spoiler free review something like that right there you go bang bang bang there's the videos and there we are number one in fact this time we even see fewer videos again we're ranked number one why am i showing you this not to show off but because a lot of you people out there a lot of you gamers guys like me guys like you ladies out there that play video games and you write reviews and you post video content to youtube you want to get more views you want to get more subscribers you want to get more promotion and you also want a second revenue stream we're offering all of that through mgn dot gg all you got to do is reach out to me get ranked like sunstrider here did let's get that ball rolling let's start getting you some more promotion some more content some more views some more money just add me on discord big space c number four seven two five my information is in the description below hit me up let's do this guys thanks for watching i've got a ton more stuff coming up stay tuned


Being a data scientist is much more than simply using plug-and-play machine learning libraries You have to really understand what the algorithm is doing first and foremost And the way you do that is by gaining an understanding of the underlying maths To be a high-caliber data scientist, you need to understand the fundamental maths. That's just a brutal truth However, the maths you need is not PhD or master's level It's typically the maths you learn in your final years of high school or in your first few years of certain Undergrad degrees. So in this video, I'm going to explain to you the maths you actually need as a data scientist And some useful resources for how you learn it. Let's get into it Data science itself is quite a big field and it's also still not clearly defined exactly what a data scientist is So if you're a data scientist in one company, you may be doing completely different work to a data scientist in another company And what this means is that the maths required for different roles and different companies will ultimately vary However, there are a few key concepts and core ideas in mathematics that I think every data scientist should know and will likely cover Anything you'll get in certain job descriptions or any interviews you may have Now it's important to mention that in this video will cover things that are mainly used for junior or entry level positions It's not for people who want to be a machine learning researcher at open AI for example That's a completely different kettle of fish and personally I have no experience with that So I can't really speak on it. So in general, there are three topics you want to cover which are statistics and probability linear algebra and calculus Those are the three main mathematical fields that are used frequently by data scientists and the ones I use pretty much day to day these fields individually are ginormous and people dedicate their whole careers and Basically life to studying them. The goal of this video is not to You know learn everything in these areas But just the things you need to know and the core principles that you will use frequently as a data scientist Anyway, let's dive in into these key concepts and I'll let you know what you need to know about it Each section will be broken up into what to learn and how to learn so you can easily tailor your learning for your data science Row map if you have one in My opinion probably in statistics is probably the most important one out of the three as is the one you Frequently use the most and it's also the most applicable to the field Like I just said probability statistics is a very large field and a lot of active research going on But we could probably break down what you need to know in this area to five or six key principles Now the first one is descriptive statistics and this is all about Summarizing and getting some key information from the data So the things you should really learn are mean Median mode Variance standard deviation Quantiles just anything that summarizes data and also very useful to have some knowledge about how you visualize it So things like box and whisker plots bar charts line graphs pie charts, you know The list goes on but you get a picture just things that you would frequently use to show Stakeholders or people what your data really means under the hood The second one is to have good knowledge of some of the most common probability distributions So things like normal Poisson gamma Binomial these distributions come up a lot in data science and also very important for any EDA project You do and also any modeling because in modeling you got to know what distribution the data has so you can fit the correct Algorithm to it the third area is probability theory now machine learning even though it's called machine learning I love it comes from like a statistical learning theory And so if you know the probability theory very well, you will also understand machine learning very well The areas you should learn for this are basically things like maximum light estimation and Bayesian sticks these are just all encompassing statistics all knowledge that you should have as a data scientist The next one is hypothesis testing and confidence intervals So these come in handy for AB tests and AB tests are used pretty much everywhere in data science and Analysis and marketing and so it's a very useful skill to know Hypothesis testing is a form of just testing significance or if your result is statistically significant Like I said, it's used everywhere. So something you should really develop and understand The things you should like learn are basically z test t test Chi square test and what are confidence intervals? The final one is the idea of modeling and inference now like I said machine learning is pretty much based on statistics And so with that you get things such as linear regression and generalized linear models These are two quite famous modeling techniques that pretty much lay the bedrock of most of the machine learning algorithms So learning those will give you a really good foundation for anything else you do from there There are of course many other areas to explore within those sub-domains and if I listed out everything You need to know in statistics, it would be pretty exhaustive and to be honest It would be a very long video. However Wikipedia has got great article that gives you a link of the whole field So I really recommend you check that out if you want a really big overview of what statistics and probability looks like as a whole Spectrum of the domain probability in statistics is such a big field and so there's so many resources out there for you to learn from However, there are a couple that I really recommend and that basically just a one-stop shop for everything You probably would need to know in the field for data science The first one is the textbook practical statistics for data scientists Now I can't recommend this book enough because it's basically what we're after it's a statistics book Exactly designed for data scientists And so it will cover all the things we will need to know on a day-to-day basis and all the things I just listed in this video if you'd rather learn from a video format Then I really recommend free co-cams video on statistics. It's about eight to ten hours But it will give you a rundown of everything I just mentioned in this video They need to learn about statistics and probability Calculus is the heart of how machine learning algorithms actually learn the optimization process of Machine learning is done through calculus So it's really essential you understand the fundamentals and what calculus is really trying to do under the hood There are two main areas for calculus integration and differentiation, but let's break them down a bit further differentiation is all about Breaking something down into small pieces and see how it reacts to little changes or rates of change Now I appreciate that sentence may seem a bit arbitrary to you right now But I promise you watch us are studying and basically understand what differentiation is and make a lot more sense to you the things you should learn are Obviously, what is differentiation and what is derivative and what do they mean learn the derivatives of common functions things like sign cosine tan H The exponential and basically why are these derivatives the way they are What are turning points and why they are important and how do maxima and minima relate to them learn? differentiation operations such as Product and chain rule which are used a lot particularly for grading descent and back propagation Which are used pretty much in every machine learning algorithm Understand partial derivatives in their role in multivariate calculus again This is a life blood of grading descent So it's something you should really be familiar with and really understand be able to understand the difference between the convex and non-convex Function again, this is really important for optimization problems and being able to understand the solution You find is indeed the best one and finally make sure you learn about Hessian and Jacobian matrices These are used throughout deep learning. So I really recommend you learn them now. Let's move on to integration and Integration is arguably used less in data science than Differentiation, but it's still equally as important. So like with differentiation You should begin learning integration by basically understanding what it is what is purpose and how does it work? Learn the integration of common functions again side cosine the exponential of the common ones You should know different integration operations such as integration by substitution and integration by parts How do you do integration calculations for volume or areas and finally? There's concepts a bit more advanced and not really a requirement But understanding what Fourier series are and its applications now the way I recommend you learn this required calculus It's through a textbook mathematics for machine learning Arguably this book might be a bit overkill and it has a lot of more in-depth and quite advanced topics in there But if you really want to learn everything in that calculus section, then your knowledge of calculus will be excellent Likewise free co-camp has a calculus 101 course So feel free to watch that if you prefer video format for your learning And so the last topic is linear algebra Which is all about how matrices vectors and scalars work in linear spaces, right? So the things you should learn in linear algebra are vectors So what are vectors?  Just basically how vectors work and what they represent then learn about matrices now matrices are basically just big You know array structures You can think of if you come from a computer science context and they hold data So a data set with n features in the emeralds is basically an n by m Matrix so it's really common and really use a lot every day even if you don't think about it After you've learned about matrices. I'll then move on to their operations and transformations So things such as the trace transpose Determinance the inverse just how makes these work and some of that baseline Transformations that you come across frequently as a data scientist for example Finding a major sees eigenvalues and eigenvectors is the bedrock Understanding you need to really grasp what's happening in principal components analysis Which are very common algorithm use in industry and finally the last topic you should cover in linear algebra is systems of linear equations This is used primarily in optimization problems where you have multiple objectives and constraints and your goal is to find a Solution to those multiple equations you should learn techniques such as row reduction elimination and Kramer's rule to learn this linear algebra I again recommend the mathematics machine learning textbook. It's amazing and like I said probably goes into more detail than you need But it gives you a really good and solid understanding of the mathematical concepts in a really detailed way There is also an excellent Coursera course called linear algebra for data science and machine learning It basically does what it says on the tin and I really recommend you check that out If you like a core structure for your learning and finally like of all the other sections Free code can have got excellent video linear algebra if you prefer video format learning I know maths can be scary and some people may not be naturally good at it But like I said the maths is not an extraordinary high level It's yes can be kind of their entry level for most university courses But I truly believe most people can learn it give enough time and enough effort The three key areas you need to know are statistics linear algebra and calculus Having a really good understanding of these three fields will set you off for success later on in your career So I really recommend you invest time right now to learn them thoroughly and really understand what's happening under the hood of all of these areas If you enjoy this video and want to learn how you become a great data scientist Then I highly recommend you check out my newsletter Dish in the data every week I give tips and advice of how you become a better data scientist And learnings i'm having my own career that may benefit you I will link it in the description below in case you want to check it out Make sure you like comment and subscribe and i'll see you in the next one


Good afternoon, the First Right of Business. It's afternoon. It's time for reflection, our time for reflection leader today is the Reverend James Bogg, the Minister of Broom Parish Church of Scotland, Newton Merns. Presiding officers, members of the Scottish Parliament, thank you for this opportunity to address you this afternoon. It's quite common nowadays when driving into a town to notice that it's been perlwyd i ddweud gan unid o'r ddechrau aeith i'r rhoedd yn gweithreff. Rwyf ni wrth gwybod i gael ei gyrraeth anodd i ddweud â'r ddweud arddangos, ein hanref yn cymorth, oedd o'r populadau o'r unid, o'r t wisio i'r rhaglion. Dwi'r fath, mae'r cwngregsio'n blinkwr o'r ddweud yn ddyddol peirio'n gweithreff oedd o'r ddweud o'r hamd, oedd o'r ddweud, oedd oedd o'r ddweud yn esechio'nug any way, but perhaps twind more by way of sharing experiences or perhaps assistance. It may be financial, material or personnel. It's a two way exchange as each can share what the other has. My own congregation has recently become involved in twinning, but not with another town nor congregation but twinning toilets. When it was suggested that we might consider this, it was done so rather hesitantly. We wondered how it would be viewed. Would it be taken seriously?  Well, it has and it isn't. I'm very pleased to say that the idea has been embraced and supported heartily by our congregation and indeed others. It's very humbling to be made aware of the dreadful and sanitary conditions that some people have to use or more accurately have to endure. Each week we are asked to spend a penny. In other words, to drop our small change into a modified toilet bowl. For every £60 or £240 raised, that will provide one single toilet unit or a complete toilet block for a school or community, maybe in Chad, India, Pakistan or Uganda. We are all aware that poor sanitation needs to disease and death and in this toilet twinning project we are helping with our small change. We are helping vulnerable people at the most basic level. I can still remember very well indeed the outside toilet that we shared with the entire close where I lived as a boy in Port Glasgow and that was over 50 years ago. What we had then was far, far better than some people have today. In the cutting thrust of politics, industry and commerce it's easy to forget or perhaps overlook the most basic of human needs. May I humbly suggest that at the next time you notice a town twinned with another that you pause and think of something much more basic and to note that even our small change can make a big change.


Live from Miami, Florida, it's theCUBE. Covering IBM's data and AI forums. Brought to you by IBM. Welcome back to Miami, Florida, everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events and extract the signal from the noise. We're here covering the IBM data and AI forums. Scott Hebner is here. He's the CMO and, sorry, VP and CMO of IBM data and AI. Something like that. Yeah, right. I know you as the CMO of the data guy. So, welcome. Welcome to theCUBE. Thanks, always great to be here. Great event. I mean, I'd never attended one of these before, this sort of analytics university. I mean, 1,700 people, everybody's like sponges trying to learn more and more and more. 60% higher attendance than last year. Awesome. Congratulations. A lot of interest. So, if we go back a couple of years ago, you know, maybe he talks about digital transformation, people roll their eyes, they think it's a buzzword, but when you talk to customers, it's real. They're trying to transform their business and data is at the center of that. So, if you go back to like 2016, there was a lot of experimentation going on, kind of throw everything against the wall, see what sticks. It seems, Scott, based on the data that I see, that people are now narrowing their bets on things like AI, automation, machine learning, containers. What are you seeing from customers? Well, I think you framed it well. I mean, if you kind of think about it, digital transformation's been going on for almost 20 years, with the advent of the internet back in around 2000, late 1990s, everyone started getting on the internet, doing business transactions, and slowly but surely digital transformation was taking effect, right? And I think clients are now shifting to what we can call digital transformation 2.0. What's the next 20 years going to look like? And our viewpoint from what we're learning from our clients is, if you think about it, it's data that fuels digital transformation, right? Without data, there is no digital transformation. There's no digital, because it's all data-driven, evidence-based decision-making, using data to do things more efficiently and more effectively for your clients and your employees and so on and so forth. But if you think about it, we've been using data as a way of looking to what has happened in the past or what is happening now. And clients with digital transformation 2.0 want to shift to a word of predictive data. How do you predict and shape future outcomes, right? And if you think about it, it's AI that's going to unlock predictive data. And that's why we see such an intense focus on AI as really the lynchpin of digital transformation 2.0. And of course all that data needs to be virtualized. It has to sit in a hybrid cloud environment. 94% of clients have multiple clouds. So if AI unlocks the value of the data in predictive ways, the cloud in a multi-cloud environment is that platform that has to be built upon. And so that's why you see this enormous shift to AI in terms of an investment priority along with hybrid multi-cloud. So I like this point of view, this digital transformation 2.0, because what's interesting in a business and a digital business, it's how they use data. And IBM's mission, at least in your group, is to help people better take advantage of data to drive business outcomes. I mean that's pretty clearly what you guys are doing. There's 2.0, to me, the innovation cocktail is data plus machine intelligence or AI, and then you scale it with cloud. And so you talk about cloud 2.0 really involves this predictive sort of component of the equation that you're bringing into it, doesn't it? Yeah, when I think of this next phase, there's several things that clients are trying to achieve. One is to predict and shape future outcomes. Whether it be inventory, whether it be patient care, whatever it may be, a customer service call, you want to be able to predict what the call's going to be about and what the client or what the customer has gone through before, what their issue may be, right? So this notion of predicting and shaping the outcome. The second is empowering people to do higher value work. How do you make them better at what they're doing? The superpowers of being aided by a machine, or some kind of software that's going to help you be better at what you do? And then of course this whole notion of this automating task that people don't want to do, automating experiences in intelligent ways, this all adds up to like new business models, right? And that's where AI comes in, that's what AI does. And so I do think it's a linchpin. And what clients are looking to invest in is this notion that you need one unified platform to build upon for the future. That is cloud services, data services, and AI services, all is one thing. One cloud native platform that runs on any cloud and completely opens up where all your data is, you run your apps wherever you want to run them, secure to the core, right? And that's what they're looking to invest in. So you guys use the sort of tagline, you can't have AI without AI, AI being information architecture. So for years in the queue, we've been talking about bringing the cloud model to your data, because you don't want to move data around. Now you're talking about bringing machine intelligence to your data wherever your data lives. So talk about why that's important and what IBM is doing both conceptually and from a product standpoint to enable that. So the number one issue with AI and actually the number one issue that sometimes results in failure with AI is they don't understand the data. Some 81% of clients do not understand the data that they're going to need for their AI models. And if they do understand the data, they don't know how to make it simple and accessible, especially when it's ever changing. And then you have all the issues of compliance and quality and, you know, is it a trusted set of data that you're using? And that's what you mentioned about there is no AI without an IA, which is information architecture. So it starts there. Then two, to your point is data is everywhere. There's thousands of sources of data, if not more than that. So how do you normalize all that, virtualize it, right? And that's where you get into this one platform, any cloud, so that you can access the data wherever it sits. Don't spend the money moving things around, the complexity of all that. And then finally, the third thing we're looking to do is use AI to build AI. Use AI to actually manage the life cycle of how you incorporate this into your business. And that's what this one platform is going to do, right? Versus enabling customers to piece together all this stuff. It's just, it's too much. So this is what CloudPak for data is and does. Yes. So when you say AI for AI, you're talking about picking the functions and automating components, prioritizing how you apply those algorithms, is that right? Yeah, so I think we talk about data. The three big things to really focus on is data. And that is the whole notion, you need that information architecture that's ready for an AI and multi-cloud world, because it's all about the data in the end, right? Two is about talent, right? Talent being skills. Are you able to acquire the skills you need? So we're trying to help our customers apply AI to actually generate and build AI and to optimize AI so they don't need as much skill to do it. In other words, democratize the ability to build AI models for your business. And then finally, the data's everywhere. You need to have completely open environment and that's the run on any cloud notion. And that's why the red hat open shift is such a big component of this. So think of clients are looking to climb the ladder to AI. Modernize their data states, make their data simple and accessible, create a trusted data foundation, build and scale new models, and infuse it throughout their business. CloudPak for data is essentially the foundational platform that gives you the ladder to AI that is endlessly extensible with things that may be important to you or certain areas of additional capabilities. So CloudPak for data essentially is the platform that I'm referring to here when we say any cloud, right? So I feel like we're on the cusp of this enormous productivity boom. If you look at the data, productivity in the first quarter went up not if you believe the Bureau of Labor Statistics, but over the long term, productivity numbers, you probably can believe in them. I think for Q1 was like 3%, which is a huge uptick. And I feel like it was much, much higher than the anemic whatever it was, one and a half, 1.7% the last couple years. All this AI, all this automation is going to drive productivity. It's going to have an impact on organizations. So what's your perspective point of view on the pending productivity boom? Do you believe that premise? How are jobs going to be affected? What a client seeing in terms of how they're retraining people, what should we expect? Yeah, and I think AI is going to give people superpowers. It's going to make them better what they do. It's going to make you as a consumer better at how you choose what to buy. It's going to make the automobile drive more efficiently and more information that's relevant to you in the dashboard. It's going to allow, when you call for service on your cable company, for them to already know your history, maybe already diagnose why you're calling and make it a more efficient call. It's going to make everyone more productive. It's going to result in higher quality output because you're able to predict things, right? And you're able to automate things in intelligent ways. So I don't see it as anything that replaces jobs. It's just going to make people better at what they do and allow them to focus on higher value work and be more efficient when you are making decisions, right? And that will result in higher productivity per worker, right? I mean, we've certainly heard examples today of customers that are doing that basically. It's not like they're firing people. They're basically taking away mundane tasks or things that maybe humans would take so long to do and then pointing that talent somewhere else. To higher value. So you're seeing that in your client base. It's starting to hit today. It's going to be interesting to see whether or not that affects jobs. I mean, we'd like to say that's not, I ultimately think it's going to create more jobs. There may be some kind of dip where we've got to retrain people. Maybe we have to change the way in which we do reading, Beth Smith and I were talking about reading, writing, arithmetic and coding. Maybe one of the skills that we have to bring in. But ultimately, I think it is a positive and I'm sanguine and I'm an optimist. But you're seeing examples today of people refocusing their talent. What are they focusing that talent on?  Well, again, I think it's just getting people to be better at what they do by giving them that predictive power, the superpowers, to be able to do their job better. So it's going to make people better, not replace them. So as consumers, we're probably going to buy more, right? You're going to buy more? You're going to buy the right things more? And the right things are going to be there for you to buy, the right sales, because everything is going to be able to better understand patterns of what happens and predict, right? And that's why you've seen this enormous investment shift among technologists at companies. What was it? MIT Sloan and the Boston Consulting Group just came out with a study, I think a couple of weeks ago, 92% of companies are looking to expand their investments in AI. Gardner came out with a study of CIOs and they're in top investment areas. Artificial intelligence was number one, data and analytics was number two, which is the information architecture, right? One and two, that's the first time it's been like that. And I think it's for this reason of digital transformation, the predictive enterprise, if you will, and just helping everyone be more efficient, more productive in what they do. That's really what it's about. Not so much replacing people. They're thinking of robots and things like that. That's a small part of what we're talking about here. Well, even when you talk to people about software robots, they love them because they don't have to do these mundane tasks and it dramatically impacts the quality of what they're doing. But again, it frees them up to do other things. Good example, LegalMation is one of our clients that we've been working with. And they do case law for business clients. And sometimes it can take weeks, if not a month, to prepare case law documents. They're able to do that in hours now because they have artificial intelligence. The background is doing a lot of the case law intelligence and finding the right data and the right case law and helping to populate those documents where they don't have to do all the research themselves. So what does that do for the lawyer? It makes them better what they do. They can shift to higher value work than just preparing the document. They can work on more cases. They can spend more time on the subtleties of the case. I mean, actually that's a good example of what we mean here. It's not replacing the lawyer. Well, I've seen a lot of examples of like this in legal fields, also auditing. I've talked to a number of us. You think you'll be able to cut the auditing bill. And the answer is actually no. Because the point you just made is they're shifting their activities to higher value. They might be charging more for activities that take less time. So. You know, customer services is another great example. There's just so many sub examples of that. But it used to be if you called, everyone's treated equal, right? And you get onto a call and then sometimes it's very rudimentary things. Sometimes there's got to be a way to prioritize what are the most critical calls, knowing that there's something already wrong and you know why they're calling. And if you can shift your human agents to focus on those and let AI help with the more rudimentary ones, you're making the clients happier but those people are doing higher value work. And we can go on forever and ever on just different examples across different industries and different businesses of how this is really helping people. And it all comes down to the three big words which is prediction, automation and optimization. And that's what AI is going to do. And with digital transformation, it just shifts the whole notion of using data for evidence-based decision-making and what's happened in the past and what's happening now to, I'm going to understand and shape the future. And you can do so many things with that. It's amazing when you think about it. I mean we've been at this computer industry 50, 60 plus years and you think everything's automated. It's not even close. All this technology has actually created so much more data, so much unstructured data. Actually so many more inefficient processes in a lot of ways that now machine intelligence is beginning to attack in a big way. You won't find a survey of businesses where AI is not a top aspiration. The trick is how do you turn the aspirations into outcomes? And that's what this ladder to AI is all about. It's a very prescriptive approach that we've learned from our clients on how to kind of take that journey to AI. And a lot of things we talk about on this conversation are the real key linchpins, right? You got to get the data right. You have to have the trust in the data that you're going to be using. You got to get the talent and be able to simplify and democratize how you build these models and deploy them. And then ultimately you got to get trust across your organization. That means the models have to have explainability, understand, you have to help people understand how it is recommending these things. And then they're going to buy into it and it's just going to make them better, right? It's a whole notion of superpowers. Yeah, get that down and then you can scale and that's really where the business impact is. Yeah, they all want to get there. Now the hard part is now we got to start doing it, right? It's kind of like the internet was 20 years ago. They know they want to do business transactions over the internet and do commerce, but it didn't happen like overnight. It wasn't magic, it took, it was a journey. I think we're seeing that movie we play in here. Yeah, and in fact, I think in some ways it could even happen faster now because you have the internet, because you have cloud. So I'm predicting a very steep O-Gyve S-Curve here. All right, we'll have to leave it there. Scott, great to see you. Thanks for coming on. Any time. All right, and keep it right there. We'll be back with our next guest. Right after this short break, you're watching theCUBE from the IBM Data and AI Forum in Miami. We'll be right back.


Live from Munich, Germany. It's theCUBE, covering DataWorks Summit Europe 2017. Brought to you by Hortonworks. Hey, welcome back everyone. We are here live in Munich, Germany for DataWorks 2017, the DataWorks Summit, Fremly Hadoop Summit. I'm John Furrier with SiliconANGLE theCUBE, my co-host Dave Vellante, wrapping up day two of coverage here with Christoph Schuber, who's the Senior Director of SAP Big Data. And it was all the good of Marker for SAP Big Data at SAP Big Data's Twitter handle. You have the great shirt there, go live. And they have a nice, you guys are nice. You guys are a part, welcome to theCUBE. Thank you, I appreciate it. Thanks for joining us on our wrap up. You and I have known each other, we've known each other for a long time. We've been in many sapphires together and we've had many conversations around the role of data, the little architecture, the role of how organizations are transforming at the speed of business, which is SAP is a lot of software that powers business. Under transformation right now, you guys are no stranger to analytics. You have the HANA Cloud Platform now. We know what they're going to do about that. Yeah. You know a little bit about data and legacy as well. You guys power pretty much most of the Fortune 100, if not all of them. Which makes lots on this. Yeah, good point on the topic of some numbers. About 75% of the world GDP runs through SAP systems eventually. So yes, we know a thing or two about transactional and analytical systems, definitely.  With Hortonworks and other cloud providers, Hadoop providers certainly, absolutely. But in this case, Hortonworks, we have specifically a solution that runs on Hadoop Spark and that allows actually our customers to unify much, much larger data sets with a system of records that we do so many of them around the world for a new and exciting use case. And you were born in Munich. This is your hometown. This is actually a home gig for me, exactly. So yes, unfortunately, I'll also be presenting in English. But yeah, I want to talk German, Bavarian all the time. I wish we could help you, but we don't speak Bavarian, but we do like to drink the beer though. It's the fifth season, but a lot of great stuff here in Germany. Dave, you guys, I want to get your thoughts on something. I wanted to get you guys, because you're like an analyst, Christoph, as well. I know you're at SAP, but you're such great industry expertise. Dave obviously covers this stuff every day. I just think that the data world is so undervalued in my mind. I think the ecosystem of startups that are coming out in the out of the open source ecosystems, which are well defined by the way and getting better. But now you have startups doing things like FinTech. We just had a bank on startups creating value and things like blockchain on the horizon. Other new paradigms are coming on is going to change the landscape of how wealth is created and value is created in charge. So you got a whole new tsunami of change. What's your thoughts on how this expands? And I'll see, certainly Hortonworks has a public company and Cloudera is going public. So you just have to see that level up in valuation. But I still think they're both undervalued. Your thought? It's not just the platform, right? And that's I think where Hadoop also came from. The legacy of Hadoop is that you don't have to really think about how you want to use your data. You have to don't think ahead, what kind of schema you want to apply and how you want to correlate your data. You can create a large data lake, right? That's the term that was created a long time ago that allows customers to just collect all that data and think in the second stage about what to use with it and how to correlate it. And that's exactly now we're also seeing in the third stage to not just create analytics, but also creating applications instead of analytics or on top of analytics correlating with data that also drives the business, the core business from an OLTP perspective or also from an OLAP perspective. I mean, Dave, you were the one who said Amazon's a trillion dollar TAM, a trillion dollar, the first will be the first trillion dollar company. And you would kind of, but you looked at the thousand points of life what cloud enables all these, if you aggregate it all together, what's your thoughts on valuation of this industry because if Hortonworks continues on this pure play and they've got cloud error coming in, they're doing well. You could argue that they're both undervalued companies if you count the ecosystem. Well, we always knew that big data was going to be a heavy lift, right? And I would agree with what Kristoff was saying is that Hadoop was profound and that it was, you know, no schema on right and ship five megabytes of code to a petabyte of data, but it was hard to get that right. And I remember something you said, John, at one of our early SAP sapphires.  And you were talking about bringing transaction and analytic data together. Again, something that has only recently been enabled.  I think that now that big data sort of entered the young adulthood phase, we're going to start seeing steep part of that S-curve returns. And I think the hype will be realized. I think it is undervalued. Much like the internet was, it was overvalued and nobody wanted to touch it and then it became, actually, if you think back to 1999, the internet was undervalued in terms of what it actually achieved. I think the same or similar thing is going to happen with big data and since we have an SAP guest and I'll say as well, we all remember the early days of ERP. It wasn't clear who was going to emerge as the king. Right, there were a few solutions, you're right. That's right. And as well, something else we said about big data, it was the practitioners of ERP that made the most money, that created the most value and the same thing is happening here, yeah. In fact, on that topic, I believe that 2017 and 2018 will be the big year, big years for big data, so to speak. In fact, because of some statistics, well, we just did a few statistics, right, exactly. Utilizing the value of big data, we're talking about valuation here, right? 75% of CIOs of the top 1000 believe that the next three years are more important to their business than the last 50. And so that tells me that they're willing to invest, not just the financial market, who I believe really run the most sophisticated big data analytics and models today. They had real use cases that real, with real results very quickly. And so they showed many how it's done. They created sort of the new role of a data scientist. They have roles like an AML officer. It's a real job. They do nothing else but anti-money laundering, right? So in that industry, they've shown us how to do that and I think others will follow. Yeah, and I think that when you look at this whole thing about digital transformation, it's all about data. Yeah. I mean, if you're serious about digital transformation, you must become a data-driven company and you have to hop on that curve. Even we're talking to the bank today who got on in 2014, which was relatively late, but the space at which they're advancing is astronomical. Yeah. I don't remember his name, a British mathematician who created about 11 years already that he co-ordined the phrase, data is the new oil. And I think it's very true because crude oil in its original form, you also can't use it. Has to be refined. Right, exactly. It has to be refined to actually use it and use the value of it. Same thing with data. You have to distill it. You have to correlate it. You have to align it. You have to relate it to business transactions so the business really can take advantage of it. And then we're seeing, to your point, we've got, I don't know, the list of big data companies that are now in public is growing. It's still small. Well, I mean, I just think, and this is why I like your reaction. I mean, I just think I'm reading right now some news popping on my dashboard. Google just released some benchmarks on the TPU, the Tensor Processing Unit. Basically a chip dedicated to machine learning. And so you're going to see some abstraction layers develop whether it's a hard and top processor hardware. You guys have certainly done innovation on the analytics side. We've seen that with some of the specialty apps just to make it things go faster. And so more and more action is coming. So I would agree that this S-curve is coming. But the game might shift. I mean, this is not an easy clear path. This bet's being made in big data and this potential for huge money shift of value. See, one of the things I see, and we talked to Hortonworks about this and the new president, betting all on open source. I happen to think a hybrid model is going to win. I think the rich get richer here. SAP, IBM, even Oracle, they can play the open source game and say, hey, we're going to contribute to open source. We're going to participate. We're going to utilize open source. But we're also going to put the imprimatur of our install base, our business model. We're at our trusted brands behind so-called big data. We don't really use that term as much anymore. It's the confluence of not only the technology, but the companies who, would you say, 75% of the world's transactions run through SAP at some point? Yeah, with companies like SAP behind it and others, that's when this thing, I think, really takes off. What I think a lot of people don't realize, and I've been a customer also for a long time before I joined the vendor side, and what is under-realized is the aspect of risk management. Once you have a system and once you have business processes digitized and they run your business, you can't introduce radical changes overnight as quickly anymore as you would like or your business would like. So risk management is really very important to companies. That's why you see innovation within organizations not necessarily come from the core digitization organization within their enterprise. It often happens on the outside within different business units that are closer to the product or to the customer or something like that. Is that something else that's happening too that I wanted to address is this notion of digitization which is all about data, allows companies to jump industries. You're seeing it everywhere. You're seeing Amazon getting into content, Apple getting into financial services. You know, there's this premise out there that Uber isn't about taxicabs, it's about logistics. And so you're seeing these born digital, born in the cloud companies now being able to have massive impacts across different industries, huge disruption creates great opportunities, in my view. I mean I just think that the disruption is going to be brutal and I want to, I'm trying to synthesize what's happening in this show and you know, you squint through all the announcements and the products. Really an upgrade to 2.6, a dupe data platform but here in Europe, the IoT thing just to me is a catalyst point because it's really a proof point to where the value is today and that people can actually look at and say this is going to have an impact on our business to your digitization point and say and I think IoT is pulling the big data industry and cloud together and I think machine learning and things that come over the top on it are only going to make it go faster and so that intersection point is where the AI, augmented intelligence is going to come in. I think that's when you're going to start to see real proof points on value proposition of data. I mean right now it's all kind of an inner circle game. Oh yeah, got to get the insights, we can optimize this process here and there and so this low hanging fruit but the big shifting mind blowing CEO changing strategies will come from bigger moves. To that point actually, two things I want to mention that SAP does in that space specifically. Startups, we have a program actually SAP.io that Bill McDermott also recently introduced again where we invest in startups in this space to help foster innovation faster and help also connecting that with our existing customers. SAP.io, something to look out for and then on the topic of IoT, we made also an announcement at the beginning of the year, project Leonardo, it's a commitment, it's a solution set and then it's also an investment strategy, right? We're committed in this market to invest, to create solutions, we have solutions already in the cloud and also on premise. There are a few companies we also purchased in conjunction with Leonardo, IoT specifically. Some of our customers in the manufacturing space, very strong opportunity for IoT, sensor collection, creating SLAs for robotics on the manufacturing floor for example, we have complete solutions that to make that possible and realize that for our customers and that's exactly a perfect example where these sensor applications in IoT Edge, compute rich environments come together also with the core where then system of references like machine parts for example, matter because if you manage the SLA for a machine for example, you just don't want to not only monitor it, you want to also automatically trigger the replacement of a part for example and that's where you need an SAP component as well. So that's in that space we're heavily investing as well. I think the other thing I want to say about IoT is I see it, I mean cloud and big data have totally disrupted the IT business. You've seen, you know, Dell buying EMC, HPE had to get out of the cloud business, Oracle pivoted to the cloud, SAP obviously going hard after cloud, very, very disruptive those two trends. I see IoT as not necessarily disruptive. I see those who have the install base as adopting IoT and doing very, very well. I think it's maybe disruptive to the economy at large but I think existing companies like GE, like Siemens, like Daimler are going to do very, very well as a result of IoT. I mean to the extent they embrace digitization which they would be crazy not to. All right guys, final thoughts. What's your walk away from the show? Dave, we'll start with you. Well, as I say, Hadoop is definitely not failed in my mind. I think it's been wildly successful. It is entering this new phase that I call sort of young adulthood and I think it's, we know it's gone mainstream into the enterprise. Now it's about, okay, how do I really drive the value of data as we've been discussing and hit that steep part of the S-curve which I agree it's going to be within the next two years you're going to start to see massive returns. And I think this industry is going to be realized, look back as it was undervalued in 2017. Remember how long it took to align on TCP-IP? You're the walk away. I mean, interoperability was key with TCP-IP was one of the things that made things happen. I remember talking about it. Yeah, great two megabits per second. But I mean, bringing back that, what's your walk away? Because is it a unification opportunity? I mean, is it more of an ecosystem? A good friend of mine, also at SAP on the West Coast, Andreas Walter, he shared an observation that he saw in another presentation years ago. It was suits versus hoodies. Different kind of way to run your IT shop, right? Top-down structured waterfall projects. So it's open source, hack it, quickly done, get in, walk away, make a move. Oh, suits with a waterfall, hoodies was the agile. That's correct. Yeah, yeah, okay. Correct. So I think that it's not just the technology that's coming together, it's mindsets that are coming together. And I think organizationally for companies that's the bigger challenge, actually. Because one is very subscribed, change control oriented, risk management aware. The other is very progressive, innovative, fast, adopters, these two camps bringing those together. I think that's the real challenge in organizations, not the technology. And on that topic, we had a lot of very intelligent questions, very good conversations, deep conversations here with the audience at this event here in Munich. Dave, my walk away was interesting because I had some preconceived notions coming in. Obviously we were prepared to talk about them because we saw the S1 filed by Cloudera. You start to see the level of transparency relative to the business model. One's 4.1 billion dollars in private value and then Horton worth pushing only 700 million in a public market, which what I would agree with you is undervalued vis-a-vis what's going on. So obviously you're going to see my observation coming from here is that I think that's going to be a haircut for Cloudera. The question is how much value will be chopped down off Cloudera versus how much value of Horton worth will go up? So the question is how Cloudera does Cloudera plummet or does Cloudera get a little bit of a haircut or stay and Horton work rises? Either way, the equilibrium in the industry will be established. The other option is- I think the former and the numbers are ugly. Let's not sugarcoat it. And so that's got to change in order for this prediction that we're making. Yeah, the haircut's going to happen. I think, but the numbers are really ugly. But the question is how far does it drop and how much is that a venture arbitrage or just how they were capitalized but Horton worth could roll up. But by point is that those numbers have to change and get better in order for our prediction to come true. But in your second thought, sorry to interrupt you. No, I like the debate now. I want to know where that line is. We'll be watching. But the value in, I think you guys are pointing out that I walked away as IOT is bigger here. I've already said that, but I think the S curve the S curve is you're right on. I think you're going to start to see real fast product development around incorporating data whether it's a Horton worth model which seems to be the nice unifying partner oriented one that's going to start seeing specialized hardware that people are going to start building chips for using flash or other things and optimizing hard complexities that you pointed out on the intro yesterday and putting real product value on the table. I think the cards are going to start hitting the table in the ecosystem. And what I'm seeing is that happening now. So I think, I think just an overall healthy ecosystem. Without a doubt. Any final comments? Let's have a beer. Great to see you. Have a beer.  We had some sourcrap. Have an ox and loggish. Yeah, we had the host and loggish. Dave, we'll have a beer. It's good to be with you again. Thanks to the crew. Thanks for everyone watching. theCUBE signing off from Munich, Germany for DataWorks 2017. Thanks for watching. See you next time.


Hey, this is Brett with Summit Auto in Fond du Lac, Wisconsin. You're new and used Jeep and Jeep Gladiator headquarters. Today I'm going to do the fastest walk around I can on this 2021 Jeep Gladiator Mojave Edition. This one has the 3.6 liter Pentastar V6 motor. It's going to give you 285 horsepower. It's going to get you 22 miles per gallon on the highway, 17 city with an average of 19. Color on this one is Snazbury Pearlcoat. Paint code is PRV, also known as Delmonico Red, also known as Red Velvet, and also known as Octane Red on the Dodge vehicles. This vehicle can tow up to 6,000 pounds. It has a payload capacity of 1,200 pounds as well. It is specifically designed for off-roading in the desert. It has Dana 44 front and rear solid axles. It has the satin grille, the LED lighting group, the tow package, the wireless Bluetooth speaker, premium audio, and 8.4 group, which gives you nav, has the backup camera, has the rock rails. Everything's outlined in orange on this vehicle. It has Falcon Wild Peak AT-3 tires, or ATW-3 tires. It has skid plates all over it. You get the special 17-inch painted and polished aluminum rims, the skid plates, and this one also has the leather trim seats, line spot cross-path detection, LED lighting group. That's about it. But I just wanted to do a quick walk around here. Some of my other videos on this one are pretty long, but if you want to check those out, there's one in the upper left-hand part of the screen. If you want to subscribe to our channel on the right-hand part of the screen, if you want to check out this vehicle on our website, on the bottom link to this vehicle on our website, click then, check us out. And thanks for checking out the video real quick today.


So the first thing you do is take care of yourself because we love you and you get in shape and if anything this emergency urgent I'll have a jet plane that'll bring you anytime and I won't work awfully close with you and I think that you've got a wonderful chance now to make a great contribution to your party and to the country too and I just hope that you take the leadership on it because we've got an awful nasty down here. It's got awful nasty. The bellies all asked us and they threatened all the neggers and they passed out the stuff saying we're gonna arrest you if you go bold and it is the worst thing that you've ever seen in your lifetime and we've got to keep our underlings now from going hog while and becoming better and we've got to try to unite the country and do like we did when under eyes now and at least present a united front with Gantt and we'll slug it out again in 68. Yeah. Well, I'm only to come and speak. I did get a pretty change. I got to a Hollywood actor for a friend from New York. That's on the wrong side. And you are almost like my friend in Delaware. I don't know what happened up there. Did he have a close race? He finally came through. They told me that I dropped off there on the way to New York but they told me that the governor just had 31% on the polls and Williams had 67. Obviously it turned out to be close. It did. Yeah. Well, I got caught around a little bit and he slowed it. What Delaware wants in the Senate is not a policeman but a statesman. Yeah, John's been a little rough on us, a little rough on us and he and his secretary are going to have to quiet down something they ought to be so mean to us now but I hope you can take charge of that group and I'll work very closely with you and you know the great respect and the great affection I have to you. I demonstrated that from the day that old styles told me you want to be leader. And I said never stop in this country that Arthur Vandenberg had worked with Truman and the communist beast in Turkey and I'd work with Eisenhower and the Formosa Straits and Suez and at Everduch and worked with Jack Kennedy on test-band and other things and I think that you've got a unique responsibility and you're not a gut fighter that gets pity and goes after the little things. So please get well or your country never needed you more. Yeah, regardless. Thank you.


Selective hydrogenalysis of biomass glycerol to propanedule is a promising route for the production of high-value chemicals but remains a challenge. Here, the authors find a PTCU single-atom alloy catalyst exhibits remarkably boosted performance with a turnover frequency value of 2.6 by 103 Mglycerol.multqsa1.h1. This article was authored by Leven Jong, Guichin Kui, Haesong Feng, and others.