Good morning. How's everyone? Yeah, excellent when I looked yesterday into my swag bag. I found three pants. I thought wow how unusual I love pants and it's good to see that you can you know still get them as a conference giveaways and Yeah, these days I guess most people would use dialysis and things like that But just out of interest because this talk will hook into handwriting and things like that Who's actually taking notes on pen and paper? Oh Wow, this is actually impressive.  Hey, these are Actually all the same in a row or not but this is actually what a Computer put together and you see for example if you go down here The four in the ninth Yeah, computer things. It's the same, but you can definitely tell it's not the same So we're going to look a little bit into this whole how do you actually? Train a computer to recognize handwritten digits Use Python for that and a little module called or a little project called brainstorm and I give you a little bit of the background kind of what you need to know for it. I give you a little bit of The stuff we're going to run so you're going to see a little bit of code and there's also That's for the impatient so you can go and download it now the actual example Which is very light-white and I hope easy to follow because this is really meant as an introductory talk and It's literally step on to three you run through that the rest is more or less the craft you need around it and a few test images if you don't want to scan and prepare your own months and This is not a talk about Artificial intelligence deep learning and all that stuff you read in the press and where you actually think okay Yeah, you need to be Google or Facebook or one of these this is more like the Kiwi shed version of Machine learning a really well-prepared example Really the basics if you want to extend from there there are many routes to explore. That's really a way to get into it At some point you won't get around the math But right now we won't need very much you see a little bit, but it's only to illustrate what's going on so if you think like oh Doing math Won't be that hard you don't have to leave the room yet That's my uncle shed by the way As you can see He's outgrown this hobby so the whole idea comes from biology and especially brain cells and If you look at it you have the extra the brain the axon Signals transmitted and whatnot and scientists thought okay, we can replicate that and we create these nodes and we connect them all up This is almost also where it stops your brain is way more complicated then kind of what you can Built in a computer kind of easily and on that level and there are other mechanisms at play But it's a nice analogy if you think kind of okay. How does this work because it all Works in parallel so it's not that you have like one pass through here and then one pass through there and so on It's literally that you take kind of the different nodes or neurons We talk a little bit about how they behave in the moment They're all connected up and they all interact at the same time. They all fire at the same time Going back to the idea of a signal and say okay if I have this little node I have to determine when it actually Fires kind of to the next one. So you see one node can have many inputs. Yes three it does something and fires an output How strong these connections are you can determine they're not all equal some are stronger links some are weaker links and Think of it as a white.  How much can go through here? Can't be different from this one here and in here if you think okay everything all what's coming in is accumulated in the node at some point It'll tip think of it a bucket filling up and then it pours out which means it fires kind of to the next one or Well, there isn't enough input. It doesn't fire. It doesn't actually go through and Nothing happens The makeup of these is always the same if you think of Python writing a class. It's pretty easy to write a little neuron class and then Get objects from that and you connect them all up There are plenty of examples of that on the net if you really want to go that basic But it's a lot of work in the end kind of to buy it up and get it right. So what we're going to use is also framework and What you have to do when you create kind of these big nets of nodes is actually optimize Because Somehow they all connected come with all their signals and you want to find at the end these points here kind of at the bottom Here you have only kind of two inputs parameters Think of that and Later on You can say okay. We have three and each node contributes one parameter. It's right now That would be good enough for three. We will have in our little example, which is very very small in the end 2.4 million so unless you're Living in another dimension similar to a check has got to the galaxy, you know and dimensional beings It's really hard for us to visualize kind of more dimensions than 3d. It's maybe time but Just think kind of hey the whole principle holds you can do it with many many dimensions and calculating that stuff is pretty hard and luckily Other people thought about that the libraries for that stuff around and can use that you don't have to think about it It's really standing on the shoulder of giants and just plugging it together And then tweaking it and making it work Going back to our 3d example. What we're actually trying to do is also find a minimum for us later on when we kind of train and Think of a skier in the mountains. You want to get downhill. It's foggy. You actually can't see where you're going You only see kind of the vicinity here. Okay. Yeah, this direction is going downhill You go a bit further you go a bit further and bit by bit You zigzag around you go into the valley and deeper and deeper until you can't go further and you've found a minimum could be local could be global As kind of like illustrated here because you actually don't know because he can't look too far am I on the Here in the minimum in the valley or am I here in the valley? Also if you take small steps it takes a long time you want to take big steps But if you take big steps, you can go in the wrong direction and to balance this all out Needs a bit of experimenting a bit of experiencing So we coming back to that in a moment and hopefully it will get clear then So in our case we are looking at handwritten digits Because it's actually pretty universal has been stable for Yeah, a couple of centuries are not so much like handwriting. This is actually 130 years ago and use what Keywords will be familiar with briskos kind of the shop. This is actually the original briskos and I mean and You still can read these numbers you recognize them and not too far. You'll have a much harder time reading this So we make our life really easy. We say, okay, we're only going to look at handwritten digits they're 10 of them 0 to 9 and We're going to analyze that For that we're using brainstorm. It's a library written By a Swiss AI lab and the open source that It's in Python and it has a few dependencies. I'm going to talk about that in a moment It's running on multiple platforms I got it to work on Ubuntu and Debian a couple of tips later on on that Windows much harder if you have a choice Go with Ubuntu or Debian much much easier because If you try to spin up the compiler for the mess stuff on your Windows box You have to do a bit of plumbing and it's not fun, but it works This is more or less What you have to run in the beginning Python dev headers. A lot of you will have seen that That's for building the Python extensions lip HDF 5 that's the library for File system that actually stores Atresies and numbers in a very compact and very efficient format because we're going to store a lot of numbers and so we're going to use that and then The last bit the lip open real is Yes That is the bit that does the heavy lifting in terms of math and the calculations Luckily at this point we don't have to understand that we just have to install it Which as I said kind of on Windows is not too easy I had good success with win Python Unfortunately, that's Windows Python 2.7 Not under 3 and under conda. I got it to work under 2.7 and 3.5 These packages the links are in the slides. They actually Help you kind of with the pre-compiled stuff and you simply have to put it in So with the last up with the basic linear algebra sub-programs that does the Let's call them calculations and can do a lot. It's also optimized for your hardware and We couldn't run all these calculations on kind of the Laptops and desktops you have at home and that way without that stuff It comes back to this whole idea QB shut. I mean if you have the facilities and In terms of cloud computing or the bigger ones you can do way more but here you actually can go far out to Calculating a lot of tricky stuff, especially if you have powerful graphics cards and brainstorm Can enable that you can use the GPU? GPUs on the graphics card are really good with parallel processing You can leverage that if you really really want to optimize it Alright once that's over line That's actually after you pull down The example I put up on github Literally install NumPyPillow and brainstorm I Walk you through these in a moment And I found on Debian you also sometimes have to set Environment here. It's kind of one of these things kind of install it. It should work. It should be easy and kaboom It blows up and then turns out Environment variable missing you find it somewhere on stake overflow or things like that, but yeah Here is kind of like maybe I save you an hour Or maybe your Google foods better than mine, but that's kind of like soft that it's kind of often these little things so NumPy That is on top of the BIS that I mentioned and does The metrics calculations and really stores nicely a race that you can handle slice and dice Just as an aside, I put this together if you never touched on it. You really should have a look Here NumPy give me a range 10 which is 0 to 9 and spits that out and you see kind of it has that It's an umpy object. So it gives you kind of the array and it also tells you with an empty array What it's meant to be is it a float is it integer and things like that kind of these little details You can Slice them you can make them multi-dimensional as I mentioned before kind of 2d to 3d to 2.4 million D This can handle it for you And you can still navigate it You can do things like reshaping which will come handy in a moment When we bring it together because we're going to read in images and you have kind of the row of pixels and you actually want to Arrange them things like that. So you cannot do operations like much more Really worthwhile if you haven't touched before and not having a look pillow This is the more or less newish Python image library. This is kind of what we're using to actually read in our little handwritten sample and Somewhere far far away. We have this little image Samples are quite small and I tell you in a moment where I got them from they're only 28 by 28 pixels and they're prepared and Roughly look like this background is black and Just as a simple JPEG and if you want to get this in so we can use it for the calculations simply go Open Reshape it and load it. So that's numpy. That's pillow Now we need actually the data This is often with machine learning the hard bit to get good training data Um There is tons of stuff out on the internet in all forms. This is actually one of the most common ones because it's so nicely structured and It has been used over and over again think of it as the hello world machine learning That's kind of what it's used for. It's 60,000 grayscale images originally they were Written by public servant and yes Institute and high school students, I think and They were separate and they would be actually originally used them for it's like hey The public servants are the training data and then we test the high school students didn't work very well because size and writing was quite different So other researchers actually mixed it up and made this data set out of it and There are another ten thousand A verification on top of that so actually We don't know you get seventy thousand twenty by twenty eight pixels each and They're normalized which means kind of from the bigger samples from the bigger scans to talk They actually made sure because it might have been blue pen. It might be red pen whatever background always black But just white. It's a grayscale. So apart from gray values, you have nothing in there. It's 28 by 28 pixels and The actual digit is kind of like centered or waited around So it sits consistently Kind of in this 28 by 28 square a lot of work to prepare So if you do that stuff with your own data or it might make your own experiments, you'll discover You're getting sick of Photoshop or image magic pretty soon But that is kind of a really really important step to get this to work Right step one now. We are starting to go through a bit through the code. It should be as easy as you run it It says please be patient goes out to the internet downloads the packed data set for you and it creates the HD a file which I said kind of is a nice way of storing it Because at the end we kind of want to read the pixel information directly. So it actually Packs up for you in the proper file format doesn't take too long and That gives us Validation data where we actually can test so our Little program born to use this to actually learn what numbers look like there's never seen it before so use that and We also know what it is So we not only have the image with that comes a label little description. Hey, this is three This is a four and this has been verified and tested and then the actual training data 60,000 images which also have the labels Which we pumped through the network and then see hey so right or not and Then correct it accordingly That You don't do with sixty thousand and one go so we can chop it into little batches In my example, it's a hundred at a time. You can choose whatever you like So how does that now look when you wire it up? This is your prepared image Ignore that it's blue here. I just thought it's prettier. It is actually like in white and each pixel You connect to one neuron 28 by 28 really quickly 784 Magic, okay, so we have 784 neurons one per pixel and each neuron has some value zero to one depending on if it's black or gray and That'll be our input layer. That's how we get actually the stuff into the network now The next layer we use way more neurons gives us more flexibility and We connect them all up together So first neuron from our input layer Goes to all of the 1200 neurons and the 1200 is just I Decided 1200 I could have made 1600 That is again kind of where it comes down to experience how do you design that stuff? That's where you have to dive a bit deeper and don't have what works in what scenarios because it'll be at the end very specific to our use case And some stuff works well and some stuff doesn't work so well an additional optimization which I'll talk about later is that we actually Reset 20% to zero said but now It's on the face of it. We actually mess with our network. So when this is kind of going through say, okay I get this information blacks your note on fire don't fire and so on 20% of the information gets dropped This is the input layer. This is our first layer after it Which will be actually not our output layer because what we want at the end is a classification You say hey is this as three is this a four how many classifications can we have ten? so at the end we're going to have Ten neurons so it goes from our input seven eighty four because that's how many pixels we have Here we have also two dimensions. You can also think of hey, what's with the video? well, suddenly you have time sequence of images and then you get this as well which obviously Makes your input layer so much bigger already again This is a simple example But even in this network if you count all the connections here and up of 2.4 million if you're diving into Deep learning things like that. They are many many of these hidden layers in here Hundred hundred twenty and you run into also problems where they don't behave as you would expect This is simple enough that you don't have to do anything special in terms of training You can use always the more less same type of neurons things like that how they react in More advanced more complicated example to decide actually per layer how they react and how you train them Maybe you only train part of the network You preset it differently and so on But effectively for us Only thing to know is okay input layer output layer two in between and Here do 50% dropout We kill that stuff. That's all design. How does it look now encode in brainstorm? Well, not too bad Here you create your layers 28 by 28 because that's how big our pictures and One deep so for example if you have here 24 frames of video, maybe that would be 10 24 10 out and The projection is the layer before kind of the output classification. You can give it a name and The naming just makes it easier later on to actually reference it when you actually investigate your network There's tools and other stuff in there So instead of remembering some note number and things like that You give it pretty names Then we create the network layers input layer is our dropout layer with a 20% Then fully connected. That's one type of layer There are many more different types in there and you learn as you go about them But the fully connected means really from if I go back to the previous one Each learn from the previous layer gets to all of the next one As you can imagine that gets pretty heavy pretty fast, but here it's okay. So we're using that Give it a name hit one for hidden and in two and More dropout layer and that's kind of we're put out Network defined. All right time for training And my boys, I thought I give you a break at this point It is a lot of input But yeah training I mean data acquisition has one part and as I said kind of we just pulling down the prepared data A lot of work has been done for us So that's really good. Usually You would be very very busy to get that to that point already. Okay, um import brainstorm In the examples if you actually look at their web page the import SBS I thought no, it's not really good. Not in an English-speaking country. This might be okay with it So I went with that and you initialize it and you just give it God means kind of noise you just Set the It's everything randomly because you don't know where to start They just they pick something and then they initialize the trainer and some trainer class and For training you have different choice again.  It's not something.  So you can influence how fast or how long your training is because if you want to very precise very small steps It can take very long because love resources, so it's just trade-off And then I only mentioned here one Once you have the train object you add hooks. They're different hooks in here That's a pretty neat one and you'll see it in the next slide gives you progress bar So it actually tells you when it's running through training what is happening and you get so a little bit of a sense for That stuff. All right. We're running step two I'm a little example. So it starts to run and then says hey before training and And how accurate are we? Okay, randomly 0.11% Well, you have ten things to choose from So, yeah, okay pretty much random. It's what you would expect And we have something in there a hook it saves actually the network into this file That's locally and that's again in the gf5 file format Makes it very efficient. All right. We leave it running and every run think of it as an epoch and In there you see 1 2 3 we have these mini batches always a hundred samples Goes through comes to the networks and says okay my example I know it from classification is three Statistically what comes out at the end is okay the likelihood for zero is ten percent For two is eight percent and so on says how well did that go and then something is called Back propagation happens. We say okay. I actually know what the result is I know what the outcome is I have to adjust it and it goes back and adjust the waiting in the network It gets better and better and you're starting to fine tune the waiting in your whole network And you run next epoch goes through Until I stopped it here after ten Just to see how well that goes and You see the accuracy is now at 95% Not bad for ten runs and you see each run took about 30 seconds. That's not with a GPU That's just running on this old notebook so That's half decent to get to a good result around the 98 99% you need around 100 epochs I let it for 500 and I put it up into the repo So even don't have to run the training you can download it from GitHub if you want you can go straight to step three By the way, the best one not with brainstorm, but there's actually shootout because This data set is so popular. It's currently 99.77 If you can do better you should publish Um The the loss in the rolling training Which is up here is With the dropouts and the stuff that didn't get into the adjustment That's rolling kind of it's around kind of the Every batch And then you do it's kind of the validation the same and that leads directly come to the next slide Much because you want to actually compare what happens when you train Yes, your accuracy So the higher the better and you see down here the epochs kind of we're running through. Oh, it's pretty bad It's getting better. It's getting better and then starts to flatten out until all more one day Well, you never will reach one but getting close to it But the validation isn't actually getting any better. It's actually with the unknown images it doesn't hit it as well and that's where the dropout because the network learns so well the exact examples and tries to fit that exactly that it's not actually getting good at kind of the unknowns we want to train the unknowns and so By actually dropping some of the information make it not That exactly you're getting a much better result. You see kind of the validation actually as Trumping the training which overall, you know, we didn't train it as much here The curve is flatter. It takes longer to get better, but the validation is much better and That's where the dropout layers become useful And this noise we think like it doesn't make sense comes through 20 percent or 50 percent of your information away But that actually works and I've also read that story that and Kaspers was playing IVM That I am only actually one Deep blue one because of her programming error and Kaspers didn't expect the move So actually noise is in this scenario It's not bad Which amazed me when I came across it With all this putting it together adjusting it how many neurons and layer how many layers The weighting things like that training data preparing and so on The tools are there and you'll find kind of there many also very big frameworks up there I'm very proposed full stuff but still your key in terms of how you put it together how it works for you and To actually be useful for your scenario There's no universal architecture. I mean, that's kind of the holy grail It won't find something that kind of learnt anything and also where you want to get away from the training We're saying oh all this training prep Can't it be self-learning and generic algorithms and things like that kind of I looked into in terms of like, okay This could actually help So it's not human-assisted on the other hand. There is so much information around that is already classified and that can be used They can base it on so you might have to find the case there. So it's really down to you to find The right stuff, right, that's the last one and That's the whole code for actually classifying it. Let me step behind here You see we're just reading in network information from the pre-created file and you reuse that over and over and over again Also, the reason why it works nicely on your smartphone because all the hard work for our speech recognition stuff like that has been done and you only Read the trained information back in get your test image Be a bit of reshaping Run it through and said hey, what's coming out of it? Why is it not? Oh, sorry guys. I was only shifting it here. I can't actually see that sorry, I have Duplicate it. No, I understand the blank looks All right, no, I hit that on the presenter screen actually all the way no touching okay, and You run it through and then you say, okay let's what's the highest value in my classification and I would be interested and actually see okay. What did you think for in this case? Where is it my number three my three image? What was the likelihood the percentage she gave for the other numbers? And that's what the output looks like So recognized yep number three With point nine nine nine nine nine nine nine almost one. So that's I'm super sure Zero and don't ignore the minus ten which means like here. There's actually ten zeros So your point there is is is is is is is is it but so it's very very small as it's algorithm is very sure it's a three And it's actually right and the repo there are a couple of other ones Well, I have to thank my family who Was okay with me taking over the playroom with stuff where I constantly had to say don't touch. It's not for you my employer sent me here ministry for primary industries and For you yeah, you guys for showing up and showing interest and you can catch me at the conference and You can chat a bit more. Thank you very much All right, we've got time for just maybe like one or two quick questions Thank you for your talk. I was just wondering there's a lot of neural network artificial intelligence frameworks available for Python what Or when would you recommend using brainstorm in particular or what's the best thing about it compared to other Things that are available The best thing is it's actually not too big I mean you can't roll through the code base in the weekend and get familiar how it's set up There's a lot of tooling in there It It's not necessary kind of the one thing you want to put into production and the documentation isn't great I mean, there isn't too much on that. It's really literally read the code in many Many examples But it's not so overwhelming as other big frameworks And if you actually look at some where I saw a list of how popular is what? It was like pretty much down at the bottom but for me it had the cute factor and You know enough tooling enough stuff to actually experiment with that what happens actually kind of if you don't use dropout layer What happens if I use in my hidden layers only 50 neurons and how does it explode and wrecked? it has a little tool with it that actually also creates and designs networks for you and Some of the tips are kind of like in there so you can still follow along kind of what's happening And what they thought kind of when they put it in there Goes into that That's the tool I mentioned the create network from spec We come up with a specification with them. Oh, yeah, I'm gonna try that and actually does it for you And so it's not so error prone You can obviously do it yourself and you can even create your kind of like on layer types and whatnot.  Thank you. I lied. We only had time for one question turns out So, can we just all thank Holger for his great talk


Some situational live goes here. First to go. Looks like Kimbrough's about to strap up. There he is. Kimbrough wants some. Oh, Tommy. First live situation to go here. Oh yeah. Ooh, he's playing all of those. I got him. I got him. I got him. I got him.


And I'm so happy I just got my hair done and Kalia had texted me and asked me to bring my haircare products that I've been using because she forgot hers at home because y'all she just moved to a new location. So she didn't have all her supplies and I was like, okay, bet, because I've been using my IGK hair products that's been making my hair look so good and healthy, y'all. And these are the products that I use. So these are the products that I brought to the salon with me. These are the IGK Legendary Shampoo and Conditioner. These have red C algae in it so it delivers hydration to my hair because my hair is very dry, y'all, very dry. And it's just recovering from a lot of damage so I had to get a shampoo and conditioner that delivers hydration to my hair because I'm trying to grow my hair. And then while doing that, I want my hair to look healthy and hydrated at the same time. So I really been liking these products, y'all. And Kalia even said, she was like, your hair looks so healthy.  I'm so proud of my hair. So I use that shampoo and conditioner and, y'all, I have to put y'all on this hydrating hair balm. Girl, okay. So obviously y'all seen me do my baby hairs with this. So I use this on my baby hairs first and then I'll go in with my edge control to make it not move. This makes my hair look so shiny. It's instant hydration, y'all. It's a leaping conditioning balm and it doesn't weigh your hair down, which I love. I use this to slick down my buns, slick down my ponytails, slick my baby hairs down to make them just shiny and hydrated. Y'all need to get this and this has coconut oil in it. Y'all need to get this mistress hydrating hair balm by Edgy K.  So make sure y'all grab these. We use this a good behavior, kerosene treatment. It's basically like a frizz control. I just put a little bit of this on my hair and also on the tracks just to ease that frizziness. So I don't look like a frizz ball and a little bit goes a long way. You do not need a lot of product. I only use a little bit of this. A little bit goes a long way. And then also y'all, obviously my hair is out and I do like to oil my scalp every now and then at least twice a week so my hair kinda gets oily and I do sweat in the gym.  I like to use this first class charcoal detox dry shampoo. Helps erase those oils so my hair doesn't look so oily. It looks a little bit more clean. So my hair doesn't look so way down so I just use this first class charcoal. And IGK is vegan, cruelty free, no parabens and no mineral oil. So thank you so much IGK for sponsoring this video. I love your products. I've been using these products for the last couple of weeks now. My hair looks so good. It's come a long way. So make sure y'all shop the links below so you'll be able to try out your own IGK products for yourself. And thank you so much IGK for sponsoring this video. So anyways, I'm about to put my hair up and I've been to get ready for the night. I'm going to the garden room. I'm excited. But I was supposed to go to the garden room a long time ago, Aurora. And I was just late. What time is it? It's seven 10. I'm about to get ready at seven 30. But Fizz is actually on his way here because tomorrow I'm actually going out to eat with Aliyah. We're going to take pictures and I want to wear this other outfit tomorrow. So I bought this shirt. It's actually large and I obviously want my, it's a little cute and girly. So Fizz is coming over to help me style it. I think he's actually, he should be outside actually. I'll see y'all in a second and I'll let y'all see like what is he going to do? Like what does he see for me? What do you see for me? Say hey boo. What's good y'all? It's Z's in a building. Did you think I forgot your name? No, I didn't. So this is the shirt he's customizing for me. I'm excited. Y'all, if y'all need any personalization, jeans or anything like that, make sure y'all hang him up. What else should we be doing? Oh, I also bought this outfit too. Huh? So remember I said I was going to give you some? Oh yeah. Y'all need to sell us clothes too. So you could just work out one by the top and give me some clothes set right here. Yes, I need this. Period. It's the green for me. Okay.  Okay. Should I put it on? I don't choose what it is online but we didn't get it wrong. They're sold out. I had to get it from this boy that's here. His name is Rags. Rags? Oh, all right. You know him? No way. But yeah, he had the shirt. That was the last one. He only had an extra long. He taxed with you? Yes. But he be wild. And let me show you my look for tonight. All right. All right, so this is the outfit. It's like a two piece, Alice in the ring. Okay. All right. And then I'm gonna What is the top to this? Yeah. Let me go get this back in. So what's good y'all? It's your boy, official Alice and I'm back.  I'm talking to your peoples. I would. Bad. What you gonna put underneath? Nothing. I was gonna do boobs. Okay. And then this bag cause I have been worn it. And you know I'm going to the garden room. So you know this color is in the shade. Oh yeah. This. Oh, that's really cute. And then I got these clear amines. Okay. They're like center of the eyes. What bag you, oh use this at the Dior for this outfit. Yo, even if you wanted to wear that bag with this, that would still be cute. Wait, what? That green Balenciaga bag with this. With it, I know. But I don't wanna wear two of the same bags. I know it would pop and need everything. Or should I wear a different bag with that? Yeah. You should save this bag for this. For sure. Okay, so what with that? This bag. Little funny joke, you could own. I wish I had a pink bag. It's a clear shoe. Who you wanted to? Cause I really wanna do a color. I mean I could do the Dior. That, what about that box you know that, Oh, this one? No. The white one? Yeah. How you feelin'? I mean I can see how it looks.  I can really wear that, The green one? Yeah, cause I deleted the picture with that one. Where you could definitely do it. But I don't wanna take two green bags. Right cause, but you should definitely save this bag with this outfit. That's like definitely. Yeah. With the shoes? Yeah. Okay. I feel like your outfit will pop even more being there is no color than all the colors in the background.  Uh huh. You know what I'm talkin' about? Where people will be taken. I know who the car home is, so. Oh. I think I know what you're talking about. Yeah. So yeah. Okay, so should I put the shirt on? Yeah, so I would just get the feel of it. Oh. You're like, no, wear that. I was gonna wear this with the red too. But I haven't seen nobody take a picture with that bag yet. I haven't seen nobody with the mini one. It's always the big one I see. Yeah. The black, silver with the narrow on it. Wait, should I take my other shirt off you? Okay. They're not looking different. Don't fuckin' look at me. No, I'm kidding. You're not people I've seen. I'm just kidding. No, you're good though. I'm kidding. You vlogging tonight? Um, yeah. Cause I never been. So it's gonna be a different vibe. Wait, should I stay out right here or do you need scissors? I got scissors. So we want a V. Yeah. So I'm gonna sew it so it could be clean. Like down here. Okay. And then as far as fitting. So you think I should just leave it like, I shouldn't have it like cropped? You're probably trying to pants on too? Yeah. All right. I'm like, where did it go? You said, do you like trying to? They taste so good cause they do complimentary like, hand me? Do this. Cause I definitely want this to show up. Okay. Like this part. So how far did you want to crop it? Cause I don't wanna cut it like the details out of it. Where it's gonna, where it's, I could like sew it but don't like cut it. So then why don't you want to take that thread out? Okay, cool. So we'll just do it like this up until like this part. Just show a little bit of skin. And then boots. Okay. All right. Let me see something real quick. How far are you saying? Like right here.  Oh, I didn't even see that look up to the floor. All right. So you can take it back up. I know. Okay. But I would definitely want, cause I want it to be like, you know what I'm saying? All right. T1 saying, are you doing it downstairs? No. I meant like, are you doing it like here? Like here in my house. Not downstairs. I'm at the point though. I didn't know. I thought you was going to take with you. Okay, y'all. So let me give y'all a little outfit cam. So I got on this other wing two piece set. So cute, casual, cozy. And then I got this cute little mini neon Balenciaga bag to bring out the outfit.  My hair is so cute. I love it so much. But yeah, this is the outfit for tonight. I'm about to wait on Shayna to come and get me. And then we're going to go to the garden room. I want to bring my camera so bad, but I got this little bag and I don't really want to carry anything. So, but I'll probably more than likely vlog up my phone. But yeah, this is the fit. Super cute. And I had to get these tailored at the bottom. So she hooked me up. And I put boob tape on the girls so they can come out since they're not done yet. Well, they'll be done one day. Maybe when I have kids or something if I want any, but anyway. But yeah, I'll check in with y'all once we get to the garden room. Can't wait, excited. Talk to y'all since yesterday, but I'm getting ready to go. Yeah. So yesterday we went to the garden room. So much fun. I didn't get to get a chance to try their food out because their kitchen was closed.  Yeah, yesterday, I'm sorry if this lighting is terrible, but I'm doing my makeup because I'm about to meet up with Aliyah to go have sushi and ketchup because I haven't seen her.  I guess that's how it is when you get older. You know, I don't talk to my friends every single day, but when I do get to talking to them, like if I haven't seen them in a while, girl, we just be talking it up and I'm just so excited to see her and get to talking because we just be chatting it up. I said I didn't get a chance to try the food because the kitchen was closed, but I heard their food is gonna be actually anyways. All I really wanted was a fire picture, so and I got that. Shout out to Shayna because she had LED light and that was a good help because it's kind of dark in here.  No cap, but anyways, we're getting ready to go to Umi and I haven't had Umi since my birthday, but I was gonna try to take another picture today, but the weather is just not giving, but I'm gonna be okay, I'm gonna be decent. When the weather is just not cute, I just don't feel like getting dressed. Hey, baby, what you doing? Sorry, Shayna had to follow me, but this is what I was gonna say. So last night, obviously, y'all, I told y'all I wanted to get a good picture or whatever and content night, y'all, I swear to you, it's so stressful to me because obviously, I wanna get a good picture.  It's just a lot of time being wasted because y'all know time goes fast and things close. I gotta get this picture, but I wanna sit down and enjoy myself before they close. I wanna enjoy my drink. Last night, I felt like, I mean, it was fun and all, but I just felt like I couldn't enjoy myself because obviously, I love taking pictures, don't get me wrong, I love that shit, but it's just a lot of tension. That's why I can never take for granted enjoying the moment as a content creator because I enjoy living in the moment and just enjoying my time. That's why I don't really feel like taking a picture today. Perfect lip. Shallot, Silbury and Foxy Brown. Ooh, so cute. Anyway, let's do the lippy. Lippy, baby. Y'all, I got a new Ebony Woods. I'm so excited and I'm gonna pair it with this Balenciaga perfume that I got because that's Hasha. She recommended this on her perfume haul and I never like smelled a Balenciaga perfume and I feel like it's gonna go so good with these two, like these two together, so I can't wait to try those out today and see how it smells. And this is the Morphe Blower Brown. I'm about to get ready. Well, I'm about to put my clothes on and I still gotta fix my hair a little bit. Sturmy, Sturmy! Jer, Jer, Jer, Jer, Jer, Jer, Jer, Jer. Jer, we're gonna jet, tell me when to get, okay? Take me to your best friend's house. Take me to your best friend's house. Is your trying to keep up with us? Y'all, but like why does summer walker snap like this? Y'all, I'm picking up Jeremiah. We're about to be on our way to the mall. Y'all, I hate going to the mall on weekends, but it's just gonna be in and out. I have an appointment at Louie because I really wanna do a Louie look. I'm gonna like Louie sweater or something like that. I don't know. So yeah, y'all, I'm just trying to come through with my winter looks or whatever. And I gotta work later on too. I wanna make this pasta today. Tic-tac, I just feel so bloated right now, y'all. It's disgusting. Let me delete some memory because... But yeah, anyways, and he just got back from Paris as well. So I'm gonna see how his trip went. And also, y'all, I cannot find my Wilford, the perfect fit leggings. I need to get another pair. I'm gonna see if they have any in the store, in the mall. But if they don't, I'm just gonna order some more.  Look at this hoe.  Oh my God, those are strong. Wow. They smell good. The candles. Oh my God. Y'all, look, Jeremiah got me some candles. I'm gonna have the link down below. What is this called? Oh my God, I can smell that. That's... This the one you like. That's... Because you know I like baked goods. Cuffing season. Bitch, this the one. Vanilla ool. Smell it some. No, this one is it. Oh my God. But this one is my favorite. That one is my favorite. Cuffing season, y'all. Jeremiah always coming through with the gifts. Oh my God, I cannot wait to put these in my house. And in my bedroom, y'all, y'all know it's a vibe up in there. Man, I want your bed so bad. I'm gonna find you one. I got you, but...  Listen, you be lacking on my looks, sweetie. So don't even try to talk to my mom. Oh, well, you ain't getting no looks. You ain't getting no bed? No looks. See how he be doing me, y'all, but he don't do this to his famous friends. But whatever. But anyways, y'all, I'm gonna check in with y'all once we get to the mall, because I gotta drive. Jacket, this food jacket's like $4,000. So that's a cropped version of that. Did you try it on? Let's do it along. If he wore it already, I want it. Wait, what's on the other side? Yeah, thank you. Let me see. But however, I like... Do you have the pants? Oh, the men's cute. Do you have pants to go with this? Is nothing that go with this? Is nothing that go with those? Oh, I love this. Nasty. The men's cute. And that cream is cute. The cream is actually cute with this too. Yeah, okay, thank you. Cause it got... Is that blue? I sort of got it. I sent it just like that. And he was like maybe that one. For a long time? Yeah. So you ended up with this. Oh, I like that shirt though. This shirt. Oh, you his sister? Mm-hmm. I like that. What you thought, bitch? How cute. This is cute. Bitch, I pulled it. I like this. I have nothing in the bag. Oh, don't say on there. So y'all, I'm just gonna get this. If they have any other coats, I'm gonna see. Actually, I do, she got this in a medium, but a small is fine. No, that size is good. Yeah, it's a small. That size is perfect. Don't go with no size. Finish put it on the shirt. This one. Move the shirt. I know, I got that. But I wanted a small size. You gonna get this? You have to have it in a small size. Don't get this, cause I want it. I can't get it. This one. Rose, that's how much. I can see if it was black. Nah, I like the brown way better. Brown is cute, but... I just like that glow, like... The holographic. Yeah, like, this one. This shirt, bro. That's a jacket. I'm gonna wear one just like this. Y'all, I got a $3,000 coat on. Like, what's up? Like, what's up? Like, what's popping? That's really cute. I'm sure it's too damn small. It's a small. So, y'all, I'm gonna try out these combat boots. She actually has one, they're nice. I like these. Let me see. Let me see, look this way. No, not with that style. This one.  I don't open this. Absolutely not. You can't move, bro. Why are you trying to find me some accessories? But anyway, I'm gonna try these shoes on there, like these cute combat boots. You got the L.E. here, right? Yeah. I'll be back tomorrow. Morning, y'all. It is the next day. Oh my God, I look amazed. But last night, y'all, I made some pasta. It was so good. I'm gonna remake it. I'm gonna clean up my kitchen now. But I'm gonna remake it later on during the week. It was so good.  Do a little Sunday reset. It's about 8 a.m. And get my life together before I start the day. But I just wanted to check in with y'all. It was a good meal, y'all. Y'all know how pasta is like a comfort meal? Bro, as soon as I ate that meal, I went to sleep. I was so tired from running around all day and then eating that meal. Oh my God. It was so good. But anyway, let me get my life together. And I'm gonna check in with y'all later. We have some fun plans today. I'm excited. And yeah, we're gonna get cute. But let me get to doing my stuff. I absolutely nothing all day, but cleaning my house. Just had some green juice before I start this little night. I'm about to start getting ready. It's six. I'm getting ready. And it takes me about to get ready.  And this is basically the stuff that I got yesterday. And I'm having Brandon take my photos because I don't know, just to have a different look on my page and also just to be doing something. So I need to start getting ready for that cause it's six 30 and I take all day to get ready. And I don't want to be late. So, but I'm gonna take my pictures at like nine o'clock. But he didn't even text me back yet. Like, what's it giving? What's tying? But I haven't doing anything all day. I just cleaned my house and then uploaded some content to my computer so I can edit it tomorrow.  I'm only gonna stay out for a few hours. I'm not staying out late. I'm gonna get up early tomorrow and do yoga. And this is gonna be a long week for me. And I just want to stay focused this week. I want to get all my work done. Stay focused, stay out for more cause I've been spending too much money. So, yeah, I'm not doing it. So I'm gonna take a pause. But anyway, let me in the shower and get myself together and then, yeah. Talk to you later, gals. Okay, y'all. Remember when I said I was gonna get lit tonight? I'm still kinda gonna go get lit tonight but my original plans have changed. But I'm about to put a little bit of makeup on cause I'm going to divine. I was gonna go. I'm literally, I have basically diagnosed myself. I'm officially introvert. Because I just found people I don't know and then when alcohol gets involved, I'm really like, oh my gosh, should I be getting drunk around these people? Cause nobody wants to get drunk around people they don't. No, you know what I'm saying? But I find that in my hair, it's like really cute. Comment down below if I should add some blonde track scene cause I've really been wanting some blonde highlights. I just want something. I just want something to spark on my look. And also I want to just get up early tomorrow and just, like I said, y'all, I'm really gonna be focused on this upcoming week. Y'all, look at this wood comb I bought. It's super cute. I need to keep this in my purse. It really calms out my hair like really good. Like, she acting all she want me to say, yeah.  I'm about to put a little bit of makeup on and plus tomorrow I'm supposed to be going out again with Elia, so. I'm like, you know what? I can go out tomorrow. I don't like going out twice in a row anyways, so. Whatever, it's cool. I'm about to use this Elf cameo. I really like this. And I really like the concealer as well. I'm really about to just do a quick little bit. And then I'm gonna go to Zalons. And then we're gonna have a little hookah. I mean, the thought was there. The thought was there to hang out with people today, but introvert and he was just like, no. No, don't even do it. Don't set yourself up. And I'm just stopping like that.  And if anybody was affected by any of the individuals that were at Astro World that got injured or you know, passed away, I'm so sorry. My heart goes out to you cause that story is so sad. Like it's so sad and devastating. I'm just like, wow, wow. Just giving like, I don't even want to go to a concert like at all. Anyway, let me just do my makeup and listen to this album cause I'm only doing like a 10 minute video, nothing crazy. Okay, y'all. So this is my outfit for tonight. I just got on this oversized, cute little hoodies it's from Revolve. And then I just got on my wolf or leggings that I finally found cause I could not find them. And I just have on my Givenchy shark boots. Y'all, these boots are everything. Like even though I'm not really going to wear they're so comfortable. I can walk it needs they're not that tall. I'm so happy I got them in another color and I cannot wait to get them stretch. They're so cute. Anyway, I'm gonna probably vlog off my phone. Well, yeah, this is the outfit for today. Looking cute, chill vibes on a Sunday. Lovely. Hey y'all, so I'm home. I took my makeup off.  But I'm not because obviously I was doing something else.   Like I told y'all I wanted to do. I don't know what's wrong with me.  Like, not even people just niggas. And yeah, whatever.  When I'm so annoyed, I have to watch something. I don't know why. I don't know why. Some people just have those things that calm them down. What calms me down is being by myself and watching something interesting. I think I'm gonna watch a challenge. I mixed the episode this week and I love watching Big T and she's so funny. I don't know why. It just calms me down and gets me into a better mood. So I don't have to think about all the real life shape that I have going on. I put my bed into, I forgot what this is called. Hold on, my thing is blinking. But yeah, I'm about to watch, I just did some skincare and, but I'm just about to watch the challenge. But what I was saying is that what calms me down is just watching something. It's people always have like something that calms them down. What calms me down is just watching something. It could be a movie, it could be YouTube videos. Like, I don't know. It just calms down my anxiety. It just makes me forget about everything that's online. I don't know, it's crazy. Anyways, I'm sleepy. I don't know if I'm gonna end up this vlog here or start a new vlog tomorrow because I have so much work to do this week. I have so much work to do. And I just don't wanna get behind in my videos. I just really, cause I have two videos I'm waiting to get approved. And I'm just like, bitch, you could have been had those videos approved, like. And I just don't wanna fall behind in my work. So I'm gonna be on top of it. But yeah. And, but yeah, at least my skin looks good. But yeah, and I'm just so comfortable right now. My bed is so comfy. Look at the view. The view that I work hard for by myself. Nobody helps me, nothing, nothing, periods.   Good night.


On February 18, 1943, squadron leader Alfred Brenner of Toronto was flying off the coast of the Netherlands when he and his crew spotted an enemy convoy consisting of five destroyers. Rather than peeling away and calling for reinforcements, squadron leader Brenner attacked low over the waves, dropping a torpedo that successfully hit a 5,000-ton enemy vessel. Facing heavy fire, their plane was shot down but not before they sent an SOS. Alone in dangerous waters, with a life raft and few resources, Alfred and his crew sent another call for rescue by sending a messenger pigeon that they had taken from their aircraft. After two long days at sea, Allied forces picked them up in a daring rescue mission. For his bravery, squadron leader Brenner received Britain's distinguished flying cross. In the face of danger, Alfred and his crew chose to be brave. They chose to put their own lives on the line for the greater good. It's no wonder his citation reads, This officer has displayed the greatest keenness and devotion to duty. Including the eight brave members of the Canadian Armed Forces who have lost their lives in the exercise of their function this year. We will honor their memory and never forget them. Mr. President, on the occasion of the 75th anniversary of the end of the Second World War, we recognize the chance that we have to live in a country like ours thanks to the sacrifice, the service and the tenacity of the people who have lived this terrible war. They have endured the loss of brothers, sisters and friends, but despite everything, they have found hope and compassion for each other. During decades, they have continued to pave the way to build a better world and to serve the common good. And it is this same sense of duty and sacrifice that has also led our Armed Forces to participate in multiple missions to maintain peace, especially in Korea and in Afghanistan. When we need them, our women and men in uniform are always ready to support each other's support. Recently, they have proven altruism by protecting and helping our most vulnerable citizens. Our elderly people in long-term care establishments. Mr. President, their efforts and their dedication to the values of Canadians are the reflection of what we have the best to offer. Our veterans served Canada with honor and valor right across this country and all around the world. They stepped up for us, and now it's time for us to do the same for them. We don't need to wonder how we will rise to the moment because we need only look around Canada to see the answer. We see it in young people getting groceries for older veterans to keep them safe. We see it in frontline workers who, after hours of standing on tired feet, never give up as they care for our parents and grandparents, the last members of the greatest generation. We've seen it in the crew of the HMCS Fredericton and the members of the Snowbirds, brave women and men who, even after tragedy, continued to show us what service and sacrifice means. Together, we will build a better world with them. Thank you, Mr. President.


Live from San Francisco. It's theCUBE, covering Oracle Open World 2016. Brought to you by Oracle. Now, here's your host, John Furrier and Peter Burris. Welcome back, everyone. We are here live in San Francisco for Oracle Open World 2016. This is SiliconANGLE Media. It's theCUBE, our flagship program, where we go out to the events and extract the signal noise. I'm John Furrier, the co-CEO of SiliconANGLE Media. Joining my co-host this week, Peter Burris, Head of Research with SiliconANGLE Media, as well as the General Manager of Wikibon Research. Our next guest, Harry Sankar, who's the Group Vice President of Enterprise Performance. Welcome to theCUBE. Thank you. Thanks for joining us today. So one of the things that you're in is performance management, but in a different way, kind of a CFO perspective. That's right. Which, this show is all about ROI, total cost of ownership, but Oracle has a lot of software, finance software. First take a step back and spend a minute to describe what is performance management and your role at Oracle. So traditionally, performance management is really about how finance manages the overall business performance of a company. It's about things like forward-looking things like planning, forecasting, and budgeting. It's about backward-looking things like, okay, our quarter is done, how do we close the books, and how do we report the numbers, both internally for management reporting purposes and externally to the street and various stakeholders. So there is a compliance side to it and there is a strategy side to it and these things have been traditionally what is performance management. What we are seeing now is that kind of discipline is now going beyond finance into operating lines of businesses, sales and marketing and manufacturing and so on. One of the things that I think, sorry, John, I think one of the things that is really interesting, especially in light of this show, is as we go through a process of digital transformation where data becomes one of the most important assets in the business, that means that the asset specificity is financed term, the degree to which an asset has only one use starts to go down because you can program it. So marketing, sales, all the assets, intellectual property, data oriented that they've been developing over the years now can be bought under the umbrella of enterprise performance management. That is absolutely true. That is absolutely true. So how's that happening? So part of how this is happening is, let's say you're a marketing organization, you're spending $50 million on digital marketing. There is a desire from the part of the marketing department to sort of manage that spend more diligently with more discipline and rigor, just like finance manages any other line item in budget. And there is more desire to provide transparency into the business in terms of here's where we are spending it and here's where we are getting returns, here's where we are perhaps not getting returns. So that is the planning part of it. And then there is also the reporting part of it where we're seeing the emergence of the concept of narrative reporting, where you're saying, hey, look, I'm not just going to distribute numbers and charts to my stakeholders, whether it's inside the company or outside. I'm going to give them context, I'm going to give them commentary around these numbers. If there is a variance, I'm going to tell them why this is there, do I expect those variants to be there next quarter? What am I doing about it? So it sort of brings those numbers to life and I watch that back and forth that typically happens. How much is the performance management moving out of the CFO function? And I want to get your taken on how the cost and IT is becoming not just a functional shared resource, but IT is now integrated across the whole company. And Mark Hurd had tweeted yesterday on Twitter, as more CEOs and CEOs understand the potential of the cloud, CIOs are going to get a lot more help implying Oracle's going to help them. But it brings up the point that the CIO now is brought into the CFO conversation. They always have been facilities, whatnot. But now from a business perspective, their contribution is significant and now commingled, is it? I mean, do you see that trend happening and what does that mean for the software side of it? We're definitely seeing that trend happening. For example, the most important new term to come around in finance in some time is the notion of digital finance. The notion of what? Digital finance. So this is really about whether you call it digitalization or not, digital finance, digital marketing, digital sales. So this idea, this digital business idea sort of elevates this role of the CIO because as you said, data becomes a very, very important asset in terms of how you fundamentally drive innovation in your business. And so that digital notion is sort of elevating the role of the CIO. And in the context of performance management, as you see this spread beyond finance into other lines of businesses, other lines of businesses are starting to be more disciplined and rigorous in how they sort of measure their performance, how they manage their performance. There's also a need to connect the dots across. If I'm doing a marketing plan, which is an important element of my overall spend, if there is a fluctuation or change, a big change in my marketing spend, that needs to be reflected back in the finance budget. So connecting the dots and aligning the plans across different functions is becoming a big priority as well. So you're seeing a lot of important changes happening. You just said a few things.  Digital business, digital assets, digitizing your business. These are the mega trends. Data value. Data value. This comes back down to what we've been talking about all week here in theCUBE and for the past year. This is now what was once a come together, have a meeting, share, cross-pollinate, either somewhat automated but, and then manual to fully integrate it. This is probably the biggest business problem in digital transformation right now. And so, I mean, how come we're not hearing more? I mean, this is- Yeah, you know, I think that's a great point, John. At the end of the day, and what we've been talking about is that some art, this is a little bit of, you know, SiliconANGLE Media and Wikibon, we believe that digital business, full stop, is how you use data to differentially create sustained customers. That is digital business. You can say all kinds of new channels and all sort of stuff, but it all boils down to, are you using data as an asset? Better than your competitors. So, that as a basis, two things. First off, interesting that Mark Herd, we talked about earlier, just to quick aside, Mark Herd talking about how CIOs are gonna get more help. Remember when we talked about how Oracle's gonna have to bring a lot of the IT group forward into this new transformation. This is it right here. Absolutely.  I hope I'm not gonna throw you a curve ball, but it's a very, very important point. As the IT organization, or as increasingly the methods that we use to create digital assets and increasingly also products, they're iterative, they're empirical, they're opportunistic, they're agile. That's the traditional year-long budget that says you have a certain money to spend and you spend it or it goes away and you better not fail with this money. Comes under attack by agile. I know a lot of CIOs that I talked to are trying to reconcile the impedance mismatch between agile and sprints and recognizing, being opportunistic and recognizing when something isn't working, and the CFO who's still talking about annualized releases of money. So I've always felt that you could not reconcile those, you could not bring those two points if you're forward without EPM. Are you seeing that as well and how are you helping it? Yeah, we're definitely seeing this because you're absolutely right. The whole notion of let's make a budget once a year, get it right and execute on it for the rest of the year, we are seeing that sort of fading really fast. What people are saying is look, plans are made only to be changed. Let's not fix it on getting the perfect plan in place. Let's start with a reasonable plan with the assumption that it's going to tweak and iterate and change many, many times over the year. So the focus is now on less on getting it right the first time, more on how do we make dynamic changes to it in an agile fashion, just to your point. Can you then reflect those changes throughout the entire process? And into finance as the key. Back into finance. It all comes back to finance. It comes back to finance because at the end of the day, let's say, take a simple example of a manufacturing company. Finance is the language of business. It still is. At the end of the day, your business performance is measured in dollars and cents, I mean period, right? So let's say your product mix changes because your customer demand is changing, that needs to be reflected back into finance in terms of okay, are we making more money or less money? Is it more revenue or less revenue? That needs to be reflected back in. So we are definitely seeing, in fact, the tagline for enterprise performance management that we use these days is enabling business agility. So two parts to that. Driving agile decisions to your point. The second is once you drive those agile decisions, let's say I decided to expand into a new business and I did an acquisition. Fast forward six months, you need to reflect the results of that combined entity into your financial results. Do it quickly. Do it in a way that is correct and you're confident about the results and that's the job of finance. So it's agility of operations, agility in decision making, those two have to sort of come together. So here's my question. I love this conversation because I think this speaks to the full closed loop of cloud and DevOps and the innovation around agile. How much flexibility is built into the software? And I'm kind of going with kind of the database Ralph for a second. Systems of records, schemas, database because business plans can stay once a year and it's failing, I agree. I can see that failing. But also fixed schemas can fail too. Well, I don't want to add the new data in because the database can't handle it. I've heard that from developers before. Again, it slows things down. So as you move from systems of record which can be fixed and or tweaked, the engagement data is the business engagement gestures. So how is that factoring into your software? You guys see that and is this AI bot revolution and the machine learning, the smart software after engagement? Can you thread that through and explain how that fits? I mean, let's start simple and sort of get a little more sophisticated quickly, right? The first thing is we're seeing a lot more people come into the planning process than before. I mean, the old model was finance did the planning for other people. Now people are doing their own plans and sort of feeding it into the overall plan. And people are intentionally pushing that because they want plans and decisions to be made closer to the point of action. Secondly, there is a greater emphasis on driving fact-based decisions.  Throw the last year's budget out and do a zero-based budget. I mean, zero-based budgeting is not a new concept. It's been around, but it's getting a new lease of life because in sort of industries where profits are under squeeze, they're saying, look, I don't want to do the traditional budgeting. I want to go to a zero-based budget. Because they have facts that are surfacing faster. Is that kind of the problem? That's more relevant to the performance of the business. That is definitely true. The facts that are surfacing faster and therefore I want to give the tools to make use of those facts to the people who are closer to where they're surfacing. This is a digitized business in that scenario. Definitely true. Everything's instrumented. It's the value of data. We've always said on theCUBE, I mean, this is the first time in the history of business in the world that you can actually measure everything. That's absolutely true. If you want to measure it, you actually can do it. That is absolutely true. So now the CFO, which is once the measurement system, has to get integrated in. Am I getting this right? You are getting this right. You are getting this right. And the other part of your question is about, okay, how is intelligence coming into, so some of these decisions over time, if you see a pattern, they can be perhaps automated. Plan adjustments can be, maybe some elements of plan adjustments can be automated, but I don't see finance going that far. That may be taken as an input. Maybe a recommendation comes from automated intelligence and people will sort of take a look at it and say, hey, I'm going to go with this because it makes sense, or I'm going to override it this way because this doesn't take into account what I'm planning for in the next quarter. Yeah, what scares me though on the whole BOT thing, I mean, this is not a diss on Larry. I love the vision. He gets the customers all excited. He said, if they try to get to AI before they actually build the building blocks, they really can get ahead of themselves. So you can see that headrooms for sure, but a lot of companies are kind of in that planning mode. Is that true? What's this progress bar of customers right now running into this in the software? I mean, I mean, chatbots are great for certain things, but you can't really automate AI yet in everything, or can you? I think there is probably a class of decisions that can be automated, but when it comes to finance, and finance tends to be conservative, and for good reason, they definitely see the value of recommendations, value of recommendations based on data, based on real-time data, but they still want to have the controls. Got it. So that's kind of the mindset that we have seen. So real options valuations could really, really be helped by AI, but at the end of the day, you have to be able to close the books, and you don't need AI to help you close the books. It's a fascinating conversation. If I can add one quick conversation, just a quick point. As enterprise performance management starts to weave its way into other parts of the business, institutionally, does that mean we're gonna see controllers start to end up in different functions? Seriously, it's a positive point. The IoT of controllers. Is that human interface that goes along with the system so that it works together? No, it's a definite possibility, right? Because if you're planning as rigorously in marketing as in finance, and if you are measuring and reporting as rigorously in sales as you're doing in finance, maybe there's a sales controller function that becomes a legitimate need. Because at the end of the day, today, you focus so much attention on reporting your numbers to the street, you focus attention on precision and accuracy and confidence and all of that. Why is that not a requirement for internal reporting? It's the same argument when we talk about the technology infrastructure. You move the computer where the data is, you could move the controller where the action is to your point earlier. It's a fascinating conversation. Harry, thanks for sharing the insight. Love to do a follow-up on this because I think this really connects the language of business and validates the digital fabric of digitization but quick, I want to give you the last minute to give us an update on the business, how you guys are doing. This is a pretty big deal. How's your business results? What's on the roadmap? What's the sales going to be like next month? Don't want a kid, I know. Ha, ha, ha, ha. Sure, sure. I think the cloud has been a real game changer in this business. What the cloud has done is lowered the bar where we are seeing many mid-sized businesses start using performance management best practices just like larger companies. We are seeing divisions or functions inside of larger businesses using performance management software for the first time. So there's a big market expansion and we are seeing an expansion across other lines of businesses outside of finance. We are certainly seeing that. And we're seeing, you know, we introduced our first cloud software in enterprise performance management about two and a half years ago. At that time, we were not sure how the market uptake was going to be because we said finance tends to be conservative. Are they going to be comfortable doing their aggregated planning in the cloud or are they going to be comfortable doing reporting things in the cloud? You know, we've been sort of pleasantly surprised by the willingness of finance helped in part by the success the companies have had in deploying, you know, HR software in the cloud or CRM software in the cloud and so on. So the cloud has taken off. You know, we have well north of, you know, a thousand customers that have picked up EPM software in the cloud. We are very happy to see, you know, 150 deployments go live every quarter and we are seeing use cases in marketing. We are seeing use cases in HR of, you know, strategic workforce planning or marketing spend planning happen using EPM style software. So happy to see, you know, mid-sized businesses see real value from planning. So it's- And integration, good integration capabilities. Good integration. I'm glad you mentioned it. Very good integration back into, for example, if you have financials in the cloud and EPM in the cloud, there are nice linkages between the two. So four themes are very important to us. We are seeing pervasive use of EPM software. We are seeing agile operations helped by EPM software in the cloud. We are seeing connected operations, whether it's back with the backbone systems or across functions. And we are seeing people take a sort of a comprehensive view of this, whether it's across functions or across processes. This is fascinating. We could go another hour. This is really interesting topic. So I think it really highlights the fact that what we always say in theCUBE is you can provision technology faster, you get time to value. Certainly as the customers start to be creative and implemented, they get to actually put it to work and get the data around behind it. So thanks so much for spending the time on the insights on the EPM. We really appreciate it. Thanks so much. Thank you. Enjoy the conversation. Okay, you're watching theCUBE, live coverage here in San Francisco at Oracle up until 2016. I'm John Furrier with Peter Burris. Thanks for watching.