So, we understand a fair amount at this point about gases, their PV behavior, how they work when they get compressed or they expand, in particular ideal gases. We've seen equations that describe their PV behavior when we compress them reversibly and isothermally, or when we compress them reversibly and adiabatically, two different types of compression. So, our next step is to combine those two things, talk about both those types of compression and expansion at the same time, and it turns out we'll discover something fairly remarkable when we do both of those things. So, let's start with the diagram. Pressure volume diagram, we'll start with a gas that sets some initial pressure, P1, V1. And what I'm going to do is I'm going to compress that gas reversibly and isothermally down to some, no I'm sorry, expand, let the gas expand to a higher volume, V2 is larger than V1, so larger volume, so it expands to this larger volume and it's at a lower pressure. I'm doing that reversibly and isothermally, so there's an isotherm here, all the points on that curve are at the same temperature, so that's an isotherm, I'm just moving from one point on this isotherm to a different point at that isotherm. So that's the first step in what's going to be a multiple step process, so let me get a little more specific about what's going on in that step. So in this step when we go from P1, V1 to P2 and V2, that's going to be our reversible isothermal expansion. We can say how much heat is involved in that process, how much work is involved in that process, how much internal energy change is involved in that process. Because we have thought about reversible isothermal expansions already for an ideal gas, we can write down what those are. So reversible isothermal expansions, the work associated with that process is minus Nrt log of V final over V initial, so in this case that's V2 divided by V1 inside of this logarithm. It's an isothermal process, so for an ideal gas, delta U is zero whenever the temperature doesn't change, so I know delta U is zero, the work is this, so the heat has to be the opposite of this, so that's going to be positive Nrt natural log of V2 over V1. So that's just repeating what we know about work, heat and internal energy change for reversible isothermal expansion of an ideal gas. Next what we're going to do is after we've expanded the gas to P2 and V2, still on this isotherm, we're going to expand it a little more, but now instead of expanding isothermally, instead of remaining on this isotherm, we're going to expand adiabatically. So I'll draw that curve in a different color, and that curve is going to look something like this because it's going to expand to some new P3 V3. Then I go from step two to step three, that's going to be a reversible and adiabatic expansion. I know to draw the curve in this way because as we know from talking about adiabatic PV changes, when a gas expands adiabatically, its temperature drops, so it's no longer on the same isotherm, there's a different isotherm that it lives on. So here's a set of PV conditions, they're all at the same temperature of the gas, here's another set of PV conditions that are a different temperature. So the first curve, I'll say that's TH for T-hot, this one is TC for T-cold. The upper isotherm is at a hotter temperature than the lower isotherm. So I've done this adiabatic expansion down to a colder temperature. What we know about adiabatic expansions, first of all, it's adiabatic and reversible, so the heat is zero. Secondly, probably the easiest way to think about this is the temperature has dropped for an ideal gas, if the temperature changes, the change in the internal energy is going to be moles times the molar heat capacity times the change in temperature. In fact, let me not say CV because we're not at constant volume. Yes, I do want to say CV. So final temperature T-cold minus initial temperature T-hot, the change in the internal energy is NCV times this change in temperature. And again, because heat and work have to add up to the internal energy, the work must also be the same value. So those are the heat, work, and internal energy change when I do this adiabatic expansion. Now, the next step in this process, we're going to eventually get back to where we started. So the next step in this process is to climb back up this lower temperature isotherm. So now I'm going to do a compression to lower volumes, and it's going to be an isothermal compression. So step from three to four, I'm going to climb back up the isotherm to some point that I'll call P4V4 that's at this cold temperature. And now that I've labeled these temperatures hot and cold, let me remember that the first isothermal expansion took place at not temperature T, but temperature T-hot, I renamed it. This second isothermal process, the compression, is happening at T-cold. So I'm using the same equations for heat, work, internal energy of a reversible isothermal process for an ideal gas. This one is a reversible isothermal compression. The work is still negative nRT log v final over v initial. Let's label these carefully. The temperature at which I'm doing this process is the cold temperature, Tc. V final over v initial is v4 over v3, and that's what I've got for my work. Isothermal, so there's no internal energy change. Q and W have to add up to zero. So heat is positive nRT-cold log of v4 over v3. Before we go any further, I'll point out that we actually know something about this ratio of v4 and v3. Specifically, if I tell you that the next step in the process is going to be another adiabatic expansion up to where I started. So this third step where I go from P3v3 up to P4v4, I don't want to stop just anywhere on the size of the room. I want to stop at the specific place where if I then adiabatically compress the gas, I'm going to get right back to where I started. So there's a particular point on this curve where an adiabatic compression will lead me back to my initial conditions. That's exactly where I want to stop this reversible isothermal compression. That particular point, we know something about this v4. In fact, for the adiabatic expansion step, when the gas cooled down during the second step when I adiabatically expanded it, we know that from what we determined when we thought more deeply about adiabatic expansions, the ratio of temperatures, T final over T initial is the opposite ratio of the volumes, volume initial over v final raised to this power r over Cv bar. So labeling my finals and initials carefully, my final temperature in the expansion was the cold temperature. So T cold over T hot is equal to v initial over v final. So that's v2 over v3 raised to this powers. That's also the same, the difference, this adiabatic cooling from T hot to T cold, that would have been the same as if I adiabatically cooled from P1v1 down to this P4v4 or equivalent adiabatically compressed up to the hotter temperature. So this ratio T cold over T hot is this ratio of volumes raised to this power. That's also this ratio of volumes, v1 over v4 raised to the same power. So if this ratio raised to a power is equal to this ratio raised to a power, it must be true that v2 over v3 is v1 over v4 or since I have this ratio of v4 over v3, if I rearrange this, if I put v4 up here and therefore I have to take v2 over the other side, this ratio v4 over v3 is the same as the ratio of v1 over v2. Let's make sure we understand what that means. This volume relative to that volume, so this value on the v axis relative to this value on the v axis, that ratio is exactly the same as this ratio. If this adiabatic expansion doubled the volume, this adiabatic expansion or compression also doubled the volume. That's perhaps not a surprising fact, but it's one that takes a little bit of thought to convince us that we do in fact know that that's true. So when I write minus nR T cold log of v4 over v3, I could equivalently write v1 over v2, in fact I'll go ahead and do that, minus nR T cold log of v1 over v2 or if I want to exchange the positions of v1 and v2, which I do to make it look more like this one, I can change the sign out front and I'll write that as positive nR T cold log v2 over v1. Any one of these three ways of writing it is exactly the same. I like this last one best because it has a log v2 over v1, which looks a lot like this one. Same thing for heat, so nR T c log v4 over v3, I could write it as v1 over v2 or I could change the sign and write it as minus nR T c log v2 over v1. So this column is our heat, this column is our work. For this third step, the adiabatic compression. So I've expanded, expanded again, compressed now to p4 and v4. My last step is to compress the rest of the way from v4 up to v1. So for this step, the step for 4 going to 1, that's going to be another adiabatic reversible and adiabatic step. This one is a compression. Like any adiabatic process, there's no heat associated with it, any reversible adiabatic process. The work and the delta u as in the adiabatic expansion are going to be the same number. In this case, I've changed, instead of changing from T hot to T cold, I've changed from T cold to T hot. So this is just the same thing in reverse. I've gone, my final temperature is T hot, my initial temperature is T cold. So we're done with our four stage process, expand, expand, compress, compress, get back to where we started. This is a cycle, a cyclic process because I ended up back where I started. It's called specifically the Carnot cycle, the sequence of doing a reversible and then adiabatic expansion followed by isothermal and then adiabatic expansion followed by isothermal and then adiabatic compression is called the Carnot cycle after Sadi Carnot, a French thermodynamicist from the 1800s. What's special about this process in particular that we've done for the adiogas? Let's take a look at what all these numbers add up to. So if I do all four steps in sequence ending back up where I started, the total amount of internal energy change, delta U, zero and zero, NCV times cold minus hot, if I add that to NCV times hot minus cold, those two terms exactly cancel each other. So the net amount of internal energy change for the process is zero. Shouldn't be too surprising because internal energy is a state function. Whatever I do, if I end up back where I started, the internal energy at that state is exactly the same. So if I go away and come back, the change in the internal energy has been zero. Heat and work of course are not state functions. So if I add these up, I won't get zero. I won't necessarily get zero and I don't get zero. In this case, let's see, if I add up some zeros, if I add up this term and this term for the heat. Remember, this is just another way of writing this one. The total amount of heat that's associated with this process is NR. There's a log V2 over V1 that both these terms have in common. This one has a positive TH. This one has a negative TC. So I've got T hot minus T cold. If I think about the work, there is some cancellation that happens. So NCV T cold minus T hot cancels with NCV T hot minus T cold. So that work associated with the second step and the fourth step cancel. Again, remember, I only need one of these if I look at this one and this one. The total amount of work is I've got an N and an R and a log V2 over V1. This term has a minus TH. This term has a positive TC. So I'll write this one as T cold minus T hot. Again, not surprisingly, if I add up the heat and the work, NR log V2 over V1 times TH minus TC, add that to the same thing with a TC minus TH. Those two terms cancel. So this plus this is equal to 0. Q plus W must be equal to delta U. That's still true. The heat is the negative of the work, vice versa. If we think about the signs of these two results, the heat, T hot is bigger than T cold. Hot temperature is larger than the cold temperature. So this term in parentheses is positive. N is positive. R is positive. V2 and V1, that was an expansion step. So V2 is a larger volume than V1. So this ratio is bigger than one. Its log is positive. So this term is positive. The heat associated with this process is positive. The work, therefore, is going to be negative. That happens because T cold is less than T hot. So T cold minus T hot is a negative number. So for this cyclic process, I haven't changed the internal energy of the system. It has required some heat and it is associated with some work. If I think about what those terms mean and what the signs of those terms mean physically, in the process of doing this cyclic process, Q being greater than 0, that means the system absorbs some heat from the surroundings. Some heat was absorbed into the system because Q is positive. Work is negative. So that means work was done by the system. Energy is still conserved. The system absorbed heat from the surroundings and used that amount of energy in the form of heat to do work on the surroundings, specifically to do PV work on the surroundings. So putting all this in a nutshell, this process, this cyclic Carnot process, has converted heat into work. That sounds like a fairly useful thing to be able to do, convert heat into work and get some useful work out of the process. So that's what we'll do next is explore in a little more detail. What are these processes that allow us to convert heat into work?


What's up, how are you guys today? We are taking a look at lean beef patty who is a very popular fitness influencer to say the least Over six million followers on tiktok a lot of people find her attractive Very fit in shape muscular abs, and she's got almost three million on Instagram. So By far one of the most popular people in the fitness industry and The main point I'm gonna make here is that Females do not look like this without the use of hormones So we'll get into that a bit later But you know a while ago I wanted to do a video about Women getting bulky when they lift weights and girls like this are why women think that sometimes Which we kind of need to debunk So we're gonna see what she eats in a day. Good morning I just woke up like five minutes ago came downstairs to make some coffee No, and got some water. I didn't drink all of this right now. This is just left over from yesterday so conventional coffee and water is I mean Dousing your stomach with fluoride toxins pollutants just really really bad for that's very basic stuff One-on-one, you know drinking regular plastic bottle of water and hey if you need coffee Maybe some things in your diet and lifestyle aren't correct because you really should be able to just wake up and go throughout your day Without the caffeine. I am counting the coffee last time I made all you can eat I mean all that I what I ate in a day Somebody made a comment and said coffee's a liquid. You don't eat coffee. I eat coffee You're just not having coffee the way I have coffee No, I'm just kidding. I just have enough coffee where it makes a dent in my caloric intake. So I'm counting it It's something and then I'll be refilling this Later in the day. I'm not one of those people that takes Track of the water they drink in the morning. I don't have a certain amount of water I drink every morning. I just felt like I needed water. So I'm drinking water This is actually monk fruit. It says stevia, but I I just like this container. So I refilled it I'm kind of on a plastic issue right now though. So I should probably get a non-plastic plastic issue Why would she bring that up after touting her giant plastic jug of water at least she acknowledges it And if you guys are unfamiliar with the plastic, you know the amount of chemicals and the current inhibitors Would be made up for if you were using synthetic hormones Hypothetically people on steroids don't really worry about the plastic as much because they're supplementing testosterone artificially. I Can't really see what that is at least it's organic and If you go really really high quality with the coffee, it's not the end of the world You know if you're using filtered water Organic biodynamic coffee beans high-quality organic cream or organic sugar. That's fine But you know, she's adding a few chemicals here and there that they're definitely not good I recently acquired a scale to measure foods Do I recommend that everybody uses a scale or tracks their food to that degree? No, some people will take it too far and it's just not healthy as I can don't worry about me though Some people will take it too far and it's not healthy. Well judging by your knowledge your energy and the amount of time You've been doing this, you know Kind of gifted on the genetic end just goes in the gym looks like that everybody likes the way she looks when 90% plus of people will never look like this regardless of how strict they are because they don't have the knowledge or the drugs or the commitment so 170 Grams, I'm sweaty right now. If you can't tell I was running around for a second Okay, so this is Lucerne dairy farms Greek plain non-fat yogurt You know when you go dairy, especially eggs at least organic ideally you're going to a local farm. So You know mainly to reduce The chemicals the negatives and and that's the real main problem with most people's diets the bad things It's not necessarily that they don't have enough nutrients. It's that all the negative stuff the chemicals the plastics Tax the body's nutrient stores, which means then you need more anyway a little bit over but It's fine Just hit my tooth. That's really scary one time. I chipped my front tooth on a peach. She eats. She eats like me bites her teeth on the middle I Am a sensitive And then one serving of honey Now it's crazy is the difference between the store-bought stuff and the farm stuff is Like kind of bad versus the highest quality probiotic you can get. I just finished my yogurt essential amino acids Gorilla mine code beef This is actually my first time having this flavor. I we did a video on the collagen a week or two ago and Amino acids kind of same thing. It's laboratory processed chemical feed a lot waste And you shouldn't consume it and if you really do want extra amino acids Then you just want a high quality collagen or bone broth. I Had the peach mango one previously Wasn't really my favorite, but I like this flavor in the pre-workout slash nitric so This is a fresh canister so I got to fish out the scoop some things in the way I'm gonna do one scoop. I That much powder by volume of a chemical very very bad to be taking every day very bad So talented. I mean clearly sponsored.  It's so so so Obvious that like they're not in their comfort zone. They didn't really do this before Because if this was like a proven healthy diet and things you've been doing like you know the days of eating I make I mean You have information to share Yeah, this flavor is way better the other the mango peach flavor Or whatever it is. I actually like it in the pre-workout, but for some reason for the EAAs it was just not sitting well with me. So lemon lime hibiscus mint Sparkling tonic This is my first time having this. This is just for This doesn't make any sense. Is she sponsored by this Contains 1% juice. Oh, at least it's organic Finish it my chicken spray the pan with Look at that pan Red pepper flakes. Someone was telling me I should season it like it's like falling apart too Like the handles side of the pan so I get it better seasoned Salt who does this like chops up the chicken puts it in the pan and then seasons it Like like that's the I've never seen anyone do that. That's so weird. I mean, I mean the chili is a little hard on the liver Conventional regular chicken is very very bad. Very bad.  Okay.  This is like Bro science bodybuilder low carb and I guess one of the biggest myths is that the toxins are stored in the fat of the animal But they're actually stored in all the tissue the protein the fat the organ so Even when people think they're eating lean chicken breasts that oh, there's no toxins in it No, there are there's a lot of toxins in it maybe almost as much as those triskets and conventional GMO glyphosate chemical filled wheat Processed with chemicals preservatives. It's just such a bad carb source Getting ready to go to the gym. It's benching today. Got my water And my Nitric because I have a lot of coffee as we have seen one scoop. I want two scoops, but I'm running awfully low Yikes So I was actually gonna make a natural pre workouts supplement, but after doing research even on like the High-quality organic, you know ancestral alternatives the nitrates are so bad for your liver and kidneys I could not ethically sell something like that that people would be consuming every day and That combined with the creatine the protein the drug usage and bodybuilding. That's part of the reason these people are having kidney failure organ failure all these health issues so so so young this stuff is not healthy to do every day And if you need the caffeine and nitric oxide for your workout Do something else do something else fix the rest of your diet fix the lifestyle get some better sleep You shouldn't have to do this You know take some B vitamins B vitamins are so much healthier yummy Just finished my workout. It was an okay workout. I benched did some benching accessories Did you get a master for 30 minutes?  She's not taking anything when she looked she looks better than 90 percent of people in the gym 95 even 99 you know I pulled my hamstring so i'm really suffering But now i'm gonna have this barebells protein bar. This is a new flavor. I I mean a new flavor for me. I've never tried it before I don't know what so have you tried anything Because normally when you want to make a day of eating video That's going to be seen by hundreds of thousands if not millions of people and influence them to do similar things Maybe you should you know have some tried and true things And the reason I actually started making the best bars the organic rice crisp bar the chewy all that stuff The pemmigan is because I would go in the supermarket and I Not one of the bars was decent yet. Yeah, there's a few okay ones that like I would eat if I absolutely had to but Still usually full of vegetable seed oils and low quality stuff. And that's what the me picking the best ones The ones she's having that's not even organic. Oh, I can't imagine That would destroy me. I wouldn't sleep for weeks Creamy crisp is supposed to mean so I don't know what it's even supposed to taste like but it looks good Looks extra crispy It's coconut. I think here's a coconut. I like it. You know, I don't know I don't see coconut flavor anywhere in here, but It tastes like coconut to me. I'm pretty hungry like extra crisps at the top Okay, I just took a shower While I wait for my hair to dry.  So yeah, it can't be good for me Let me just measure out half a pop tart So Do I think everybody should be Tracking and measuring to the degree that I do that not necessarily for some people it can be very helpful I think a lot of people either over or under estimate how much they're eating So tracking at least even for a little while can be pretty beneficial Just so that you actually see how much you're eating because if I don't track I'd Probably not get enough protein But I might get a lot of carbs and a significant amount of fat just because naturally I'm gonna gravitate towards basically just Eating like cake if I was just gonna eat how I eat. I wouldn't take another three minutes work So I'm debating between having one of these turkey Stuffed bell peppers But I feel like that's not fun Because he didn't make it. I don't want to make anything. I want to have his chicken Yeah, we know you don't actually cook anything. We know you don't we know you go to chipotle or something every day make something This apple in the meantime Uh, I found this pasta that I forgot that I bought like a really long time ago I actually am not pasta that she bought a really long time ago for her what I eat in a day Not a fan of pasta, but When else am I gonna eat it?  I used two ounces of greek yogurt Some parsley and some Italian seasoning tzatziki sauce Then I started to prepare some vegetables had garlic and broccoli I think it was like four ounces of broccoli If I do believe correctly, it was like low key a lot of broccoli though. I didn't measure in a cup. I measured on the scale I cooked that in the pan. I just put some salt on it Cooked in the pan then my pasta Do you guys know how bad those salt grinders actually are like are they grinding plastic into the food? I've always avoided them because I've heard that but Haven't tested it myself. It was done. So I combined it in the sauce added some nutritional yeast We did a video on nutritional yeast a few years ago now Just take the vitamins Mix that in I don't think it would have made a difference if I mixed that in before after but I just happened to remember after Then I combined the vegetables and the chicken and mixed all of that together I guess you look as an Italian a little little broccoli Rob with some garlic and pasta But she butchered it. She butchered it. I would not I would not want this Uh, it's honestly, it's not that bad of a meal compared to what the average stands in American dieter eats But I mean it's not good for your liver because of the chicken and the broccoli if this was actually just Like ground beef and pasta even though conventional. I wouldn't be as against it, but Again when you're not going organic you have all the toxins and the chemicals in it when you're not even quality food It's just it's too hard to get over that hump You don't have to mix it if you don't want and doesn't really make a difference Then I enjoyed and it was scrumptious Another note I would like to add is I feel like a lot of people believe that Tracking is inherently restrictive, which is not the case because it's kind of getting late right now And if I had been left to my own devices, I would have just gone to sleep But I haven't eaten quite enough today So if I wasn't tracking I would have just not eaten enough You see what I'm saying? Anyway, I'm not gonna smoothie Or protein shake if you want to When you eat quality food and go according to your appetite If you work out your body composition will naturally get to where it is So you can make whatever you want as long as there's protein fat and carbs in the meal As long as it's all high quality and you eat to your appetite You're gonna look good and you're gonna feel good The reason these people need to do this seco calories in calories out nonsense track everything and restrict everything Is because the food's low quality. They're deficient in nutrients.  They don't have a choice Call of that I also got this recently The macros on it is it's like Basically heavy cream But I'm really interested in it and I feel like it will make my smoothie a little bit better. It's rose water yogurt drink That sounds disgusting I don't think it's gonna make this better, but I like the taste of it on its own I actually did decide to add a little bit literally every single thing in this video Is random stuff she found in her pantry That she has to use for the day of eating because she didn't go shopping apparently Rose water heavy cream. Yeah, I'm sure you eat that every is I'm trying to make this thick and creamy, but I Can never do it right.  They were all weird sizes So it's like seven but some of them are like this big um one scoop of vanilla protein powder and A little bit of this and then a little bit of water We'll see It's really very delicious much better than my ones made with just water and banana and protein powder Pretty good consistency kind of like a milkshake But not quite so sweet Wow, that was enlightening glad I wasted 20 minutes of everyone's time for this nonsense Hey, look The least they could do Is put a little bit of effort into it, you know It's absolutely insane like She didn't even go shopping. She didn't put anything together She didn't try to act or sound like she knows what she's doing Literally just minimal effort But I guess that's the attitude you have when you have like 10 million followers on all your platforms and have everything Handed to you. I mean she only has a few sponsorships does have a very large following. I mean I can't imagine she's making a crazy amount of money just from the ad revenue But she does sell the gym program So I'm sure that a lot of people are trying to look like her Despite her probably taking performance enhancing drugs having zero clue what she's doing and looking like that. So I think most of the recent day of eating reviews for the most part have All had this similar thing where they don't actually Eat normal foods and cook for themselves. They just go out and eat all the time So when they actually have to do the video, it's so fake and obviously it's not what they actually do anyway Thank you guys for joining me I don't really think this needed any explaining But I'm sure she has tons and tons and tons of people that are Listening to her and following her. I think they're going to look like this.  We should see what the comments are real quick What I really like about patty is that she's authentic and very down to earth She's like a good friend that you didn't meet yet kind of refreshing because fitness community is filled with drama And yes, that's a real comment. Definitely a real comment 100 percent real person not a robot We are refreshing to see a bodybuilder eat to diet. This is flexible and sustainable as it is healthy How is eating chicken breast and trisket? Flexible and sustainable or healthy? Oh my god Who knows how many follow who knows how many of her followers and viewers are real or fake at this point, but I think we've gotten the point across If you guys could please drop a like video leave a comment down below Subscribe so that youtube can unsubscribe you next week and be sure to check that notification bell Also, they don't notify you of my videos. Therefore as I said guys check out frank dash upon calm Thanks for supporting me and I'll see you guys for the next video


Live from San Francisco, it's theCUBE, covering Informatica World 2016. Brought to you by Informatica. Now, here are your hosts, John Furrier and Peter Burris. Hello everyone, welcome back to theCUBE. You're watching SiliconANGLE Media's theCUBE, our flagship program where we go out to the events and extract the signal from the noise. I'm John Furrier with my co-host, Peter Burris, head of research at SiliconANGLE and general manager of Wikibon Research. The next guest is Yelena Roljevic, who's the assistant vice president of Business Intelligence at George Washington University. Welcome to theCUBE, thanks for coming. Thank you for having me. So, obviously you're a practitioner in the trenches, university environment, very fluid, lot of IT, a lot of things going on from student activities to running the university. Share with us the data picture. What's it like there? What's some of the things that you guys got going on there? Okay, so higher education has lots of data. We collect information on our prospects, on our current students, on their life at the university, how they interact with library, how do they come to sport events and games that all helps us understand the engagement of a student and informs any changes in our activities that we have to make to retain those students and make them succeed and become new world leaders. So we have, the way universities have grown organically, you have data in many pockets of those organizations. And I come to higher education from working about 20 years in the government consulting business. So I was actually very surprised to find out how complex universities are. George Washington University in particular being a city school at the heart of DC, you know, it's like running a small municipality. So you can imagine, we have every data type that can be. What a silo that can imagine. And lots of silos, you know, I mean, we have at least three ERPs, one for our financial data, the other that houses our students' information, and then lots of data on spreadsheets and et cetera. So what we've done, what GW have done about 10, 15 years ago, it started to build Enterprise Data Warehouse. But it was very tactical, you know, the reporting information was really at a very tactical level and we needed to get that value from this, from data all the way to our executive leadership and other decision makers across the university. So about three and a half years ago, we started actively engaging with our business functional areas, you know, and change our project management methodology to really being fully agile, you know, we are on two week cycles using Scrum. We bought the blog as our data visualization tool. We already had IBM Cognos in the environment, but we just needed something that's easier for our end user community to use. Our goal is the BI's self service. So my team is working on integrating data from a variety of different systems, ensuring data quality. We also kicked off a data governance program and that's working very well. We tied our data governance program to our business intelligence efforts because business intelligence efforts had momentum and support behind it and to showcase the value of data governance and convince our data stewards to actually support work of data governance. We tied it to BI, so basically our mantra is if it's important and we are asked to put data in the dashboards, in the reports, then it's important data we should maintain and manage. So then we make sure before we publish these solutions that every data element and any assumptions we made are documented in our data governance tool. So you have, I mean, do the students have mobile devices that you provide in the app? So you have data coming from the mobile devices? We have bring your own device, any device you can imagine out there, you know, policy and that is very interesting challenge to manage. I think on average every student brings about six devices that includes in addition to their phone and tablet, you know, and computer, their PlayStation, their, you know, game box, their TV, Netflix. They have all their apps, like Yik Yak and all these crazy apps that are out there. Exactly, exactly. You've been Yik Yak? No. My daughter informs me what's being said about GW whenever she sees something like that. So, you know, we get all kinds of devices on our network and our students are of course digital natives more and more and they really love some of the dashboards that we are putting out there. So the edge of the network is very robust. You have end users using all kinds of apps, very diverse, you bring your own device to work. I'm sure you got a bunch of IOT, but the thing that you guys live every day is the siloed concept of different systems. Now you have remote campuses too, so you have to move across geography or. We have also lots of, we have international campuses. We have campuses in China. We have, you know, in the DC vicinity, several campuses, Foggy Bottom being our main one, but we have our graduate programs in Virginia. We have Mount Vernon campus and quite a few other locations throughout the city. So as you sit back and modernize, so the theme here is modernize your data, data 3.0, all that stuff, all that good stuff. I mean it's messaging, but the impact to you is you have real systems to traverse. Yes. You have good data and move it across different networks and or geography, certainly international. How do you guys approach that? Do you guys get on the white board? Is it pretty obvious? Is it trial and error? You said it's agile. What's the approach? How do you solve that problem? Because that's a really kind of current use case that most people face. Yeah, we do have our enterprise architecture team that helps look into current state and helps us understand what the situation looks like, what devices and applications we already have, how we move data between those applications, lots of point-to-point integrations. That's one thing, it's on my to-do goals to change. You know, using Informatica, we have PowerCenter on-prem and in the cloud and we are working with our Informatica partners on building the true data integration platform. So I was very excited with what I heard this morning at the general session. So we also work very closely with our business functional users. We try to understand their business, their needs for information, which continuously changed because higher education is also highly regulated industry. So you have those pressures, but also we are dealing with new generation. That is every day. The regulation, which is its own challenge, in and of itself, the regulation of the data. But also it's interesting is that you have real consumers. Yes. And this whole consumerization of IT, which we talk a lot about, is real. Most businesses can actually go slower than higher ed, but on the edge of the network, you've got real consumers who want consumer experience. Yes. And you get the highly regulated infrastructure. How do you balance that? What's the, how do you guys do that? It must be really hard. Well, can I add one thing before you ask about that? Because it's, your most important consumers are some of the most aggressive users of these new technologies. I think all businesses have to do this. But one of the things that's interesting about higher ed, as you said it, is that to attract students, you have to demonstrate your affinity for the way that they want to do things and translate the way learning into mechanisms and systems and programs that they find attractive and they immerse themselves in. So let's add that to the answer. And that's definitely a very complex problem. And I think the way we address that, as I said, through enterprise architecture, to talking closely with our business on a regular basis, we have these portfolio meetings. We have governance across the board, trying to prioritize the most important efforts that the business needs to put in place. You know, by trying to, having a very dedicated teams, I think having a stable infrastructure that's stabilized across several past years. And, you know, constantly innovating and constantly looking, going more agile so we can deliver on the top priorities and hopefully get enough out there to support our business with attracting students and recruiting students. We are deploying a CRM system across the board. And keeping students, absolutely. And one of the things my team is focusing on right now is the retention effort and student success rate. I mean, we're building a future leaders of the world and we want to make sure they come to GW, they have some great opportunities there. I mean, for internships, et cetera, but they also have some great opportunities to go other places. Today's students are tomorrow's alumni. Exactly. So as you think about this challenge, you're in the technology organization and it's forced you to partner with a whole range of institutions within the university. How is the relationship, your relationship, to the different parts of the university and those decision makers starting to evolve? I think we have a very good truck record as I said, I spend most of my time working with business. Although I'm physically located, the division of IT is physically located in our Virginia campus, I spend pretty much my entire week in downtown DC talking with head of enrollment, with provost, who is a big fan of data and constantly watching for trends. And it's a very, and I don't want to go because it will take us a couple of days to go into the whole process of recruiting and attracting and getting, building your new coming class and watching these melts because you know that not all students will come and planning, you're at the vim of 17, 18 year olds and you have to plan the housing for those kids, you have to plan, and of course, you have to make the class that you expect to make. So it's a very complex problem and our business constituents need lots of data. Our research, GW just broke into top 100 research institutions, our researchers need information to manage their grants. They're accountable for how they're spending their money and they need the real time information to know what their current budget looks like, how much they spend, what they have to spend, how far are they getting any additional funding to their grants, who's working on their tasks. All of those things in the past were very static and now we were able to get them some cool tools, visual insights into their information that they can use to make informed decisions and then their deans and department chairs also can look at that data and act on that information and they use data for use cases, I have never imagined we can use that. I'm curious, just a kind of side note here, just a tangent on the question is, being an IT and being in technology, you're enabling all this data and they're using data in use cases you've never seen before, are you getting pulled into the whole e-learning concept because now we're hearing that having courseware online isn't e-learning anymore. The students themselves are finding non-linear progressions to data that may or may not reside in the university and does that put pressure on the delivery of the education? Have you been pulled into that? Can you share some insight into some of the dynamics? It's a real problem, people are scratching their heads going, what's a better learning environment, the internet or? There are those debates and you can imagine, I'm learning about higher education, inner workings myself and I'm amazed by questions our faculty is asking and our administrators is asking, so we have a group that's focused on online education, I don't necessarily sit at that table, maybe not yet. But we're definitely looking in better ways to enhance students' experience and it's not just taping a course and providing it online, it's being able to interact with these students and customize their experience and answers you're providing based on types of questions and their skillset and they're bringing to the table for that course, so otherwise you lose lots of students who are probably, who cannot follow the program, which is something in the classroom you can correct. I mean the reality is right around the corner, you've got VR, virtual reality and you've got AI, kind of bots coming, I can see that kind of happening and connecting those dots, but the other thing that's interesting is that you're starting to see the listening, we heard the marketing cloud announcement here around using listening engines and we know that we've reported on our publication that on the admissions side they're looking at their Facebook posts and saying do we want that person? This is the application, they're valedictorian, looking good but also look at their Facebook or Twitter feed or Instagram feed, different data. So the digital footprint now is interesting. Does that come into the mix at all? Absolutely, absolutely. My own daughter was applying for college this year, she's a high school senior and she told me, mom I cannot be on vacation and she said I cannot check my university response because I have to come from the same IP address because in order to predict if the students really going to come, the schools are starting to look at the entire profile of that student and it's tying it to the IP address so they can say this kid came to visit in the college, this kid clicked this many times on the different information. Downloaded a podcast or a video tour. Exactly, so the space is becoming very competitive. There are all these debates out there about higher education, the value of higher education, the cost of higher education. So the university is really using data to try to predict who is really going to come and who is going to fit as well. Yes, I mean one of the things that we are looking in our retention modeling is can I predict how, what kind of experience students will have if they're coming from this high school versus that high school depending did they enroll in engineering or did they enroll in art and sciences and can I immediately offer them more support or maybe some additional classes? You can tell by the metadata if they're really interested. We have kids in high school, I have two in college and two still coming through, Peter's got a daughter and this is interesting, a lot of people say oh, this is a safety school and schools can actually figure out like okay, we know you're mailing this one in but this is the value of the data now. You can actually look at it and the other discussion in the high school is not so much academic being the best and checking off your overseas community service. So they want to fit. Fit is now the discussion in high school for colleges. Don't be so hung up on the D1 and all these different schools. Find the fit. How does that impact you guys? You guys being asked to provide more data sharing? Yeah, definitely, definitely. We need, our admission offices need more data. As I said, we are putting CRM in place because our current ways of interacting with students are failing short, trying to get that whole picture and to understand how good of a fit that student is and is it going to actually show up in summer or is it just going to melt? So I have a question that's a provocative question. So lean back and think about this one. Lean back then lean in. Lean in, lean back, lean in. Think about this. So people who are watching who are interested in higher ed and some of the innovations, what would you share with them? Spend a minute talking about some of the innovations that you guys are doing in higher ed and higher ed in general that you are excited about that could be for a parent or for a student watching that they may not know about. What are some of the exciting things that are happening innovation-wise? Well, I think in GW in particular, some of our research facilities that we are putting in place and equipped with the technology that I have not seen when I went to school and I have an engineering background, I think make me very excited. You know, I get very excited just walking into that building. Our ability to, as you said, customize their experience to their interests and needs because we have information about their interests and needs. I think that's also amazing. It's something we didn't have when I was growing up. You're one of millions people in a lecture hall. Until recently. So those things really excite me, you know. Just the sheer amount of information that we have today on their social interactions that I don't think we are tapping in yet, especially not to the degree I would like to tap in. I mean, I would like to offer incoming freshmen interesting plays at our learner center for performing arts. I want to put the right type of displays in a textile museum, but if we know more about these kids, and I mean, they're amazing by the virtue of being 17 and 18 year olds. I mean, they have all these interests. They will self-organize, they will start to dance. So provide some discovery really for them. But they live in discovery mode. They look at the world. So giving them, you know, looking at our library, for example, who also wants to understand who's coming and what services they're looking in the library so they can improve those services and maybe offer others that they are not offering today. So I think in every aspect we can help students have a better experience, graduate sooner and really become a world leader. Well, it's also interesting that there's been 50, 60, 80 years of research and investigation into how people learn and we still don't know. There's been all these different models of pedagogy. Some people learn by doing, some people learn cognitively, some people learn blah, blah, blah, blah, blah, blah. And over and over they've been proven wrong. And it's going to be interesting to actually have data that actually speaks to how people learn and over the next five, 10 years, have that be translated into real programs that are probably better attuned to how people learn. Fascinating you're at the forefront of that. How are you using Informatica? So we have Informatica Power Center on-premise that is both on-premise and in the cloud that we are using to do our ETL nightly loads. We have a data quality tool that we just recently, this year purchased and installed and we are offering a data quality services to all our constituents at GW profiling data, addressing data quality issues. We are communicating to our businesses how expensive the poor data quality is and I think they're becoming very understanding of those type of issues. We also have Informatica Cloud that we use for data integration across our different systems and to exchange information with external partners. We are looking forward to use Informatica for master data management, which is on our... Two-do list. Two-do list, exactly. So that's basically where we are with Informatica. Don't need a lot of data to choose that one. No, but as I said, we started Data Warehouse GW, was it started a couple, 10, 15 years ago. So I think we are... We did some work, we now understand our data. I think I'm hearing lots of my colleagues that are starting with master data management. I do think that tying it to the real use cases, which is if you really need this data to make decisions, that's probably important data I should care about. That's a great little rule of thumb. Awesome. Well, I got to ask the final question. What got you excited for the conversation here? Anything pop out of the keynotes that you made a mental note of that you're going to focus in on, that's exciting? Absolutely, I attended the keynotes yesterday. That was exciting on multitude of levels, hearing about data 3.0 that Informatica is focusing on. I mean, in hearing this morning and seeing some of the demos on the, you know, the data mapping and those aspects. But what excites me, another thing that excited me yesterday was a conversation between Tableau and Informatica and the partnership that they're having.  So I'm very happy to hear, to see that they're talking and collaborating because that will make hopefully our life easier. Yeah. So if they're coordinating their roadmaps, what I like about Informatica is it's focusing on data. It's really not going in every direction out there that we've seen some other vendors going. They're really focused on creating this central place where you can get the quality information. The value of data. The value of data. And when I look at my team, although we do visualizations, we are also pushing a self-service model. We want our functional users across the university to answer their questions. Business users, easily. That's one of the reasons why we chose Tableau and we have a community of PI practitioners that has about 70 users today. I mean, we have like 4,000 employees so we are not a huge entity. So 70 users is a lot of folks who can go and tap into that well integrated, high quality information and answer their own questions. Having good data governance so they understand what that information is about and how it's defined. And that's where I think the biggest value is and where I think my team provides the most value in working in that space. So our end users can really self-serve themselves and not depend on our queue and in our order of priorities. We still continue to support the broader, larger initiatives but we would prefer to have our housing department do their own visualizations using data. Because they're the customer. Exactly, they're the customer. Yelana, thanks so much for coming on theCUBE and sharing the insights and the data here in the moment in real time. Thank you. At Informatica World. Yelana Roljevic, thank you so much for George Washington University sharing the challenges and opportunities around being real time, consumerization and breaking down the silos. This is theCUBE live in San Francisco. We'll be right back.


You centered your life around education education who would never talk about education This could be really interesting, okay, you know, I've been down to Frankfurt a few times Would you mind if I recorded okay cool cool cool. I'm Ty by the way hi Ty. I'm Carol Carol C-A-R-O-L I'm only I don't think a lot of people spoke was okay, but I always like to take I know because everybody spells everything different I know right anyway, so yeah, it's just through no one. This is just a personal hobby You just decided to do this. Yeah, so I have time off from work and Normally what I like to do is just try to fill up my time with something productive And I thought man, I saw these people on YouTube having news conversations with people Yeah, just randomly just like hey you want to sit down talk for five minutes, and then people would be like yeah You want to talk about TV? I'm like sure let's talk about TV or like I Saw this thing that happened in the news. I want to talk about it. Just bounce the idea Because you know NPR was here downtown really a few years ago And I remember what that program was called and that's what they used to do okay So like a story core it was that's exactly who it was very similar to that and I'm thought that sounds so cool What do I need to do to try that out? Many people stop yeah, so I've been doing this since November. I got a nice little channel set up Yeah, I got backlog videos until like April. No wait wait not April What's it? What's month after May June so like June? Yeah, so like I just I found it a really rewarding way to just have a Meaningful conversation with people about whatever they want and I also learned how to like Not get anyone you know defensive or anything like that my goal is just to have like a really positive conversations I'm California originally. I've moved around a lot of military actually Military brat my mom. I've never been in the military, but my mom that's my husband's a military brat What brand thing his dad was in the army, okay? Yeah, and I ended up living all over the place Yeah, I went to like three high schools all that stuff So you learn how to make friends really quickly and that's why I think stuff like this is important You have a topic you want to talk about I'm not really anything that really matters See anything that motivates you something that you strongly believe to be true. Maybe something Centured your life around education education who would never talk about education This could be really interesting, okay, you know, I've been down to Frankfurt a few times recently That's the capital of Kentucky. Oh, you know, I'm learning things. Okay, look check that out So Frankfurt are they known for their education or is that? No, but you see we've had a governor here that We don't see eye-to-eye with him. Okay, so we've gone to protest, you know old-fashioned like in the 60s and 70s Could you walk me through the issue? I'm not I'm not up to date the Well, it was simply that our governor wanted to change Some of our retirement benefits and all like that for education and I don't feel that he's really supported public education and So a lot of school districts shut down and went to Frankfurt and Fayette County was one of them, okay So just so I can catch up sure there were schools that were here But the retirement plans for teachers got pulled or like defunded by the governor and changed around Okay made more inconvenient right right and because of that a lot of school systems of good ones moved to Frankfurt just to get Well, no, no the people their teachers and staff faculties went to protest. Okay, you know with signs to pick it Yeah, yeah, yeah, yeah. What was the result or is that so long ago? It's still ongoing Bevan didn't seem to care what any of us thought and so now our Andy the sheer which is our state's attorney is gonna fight him in court. Okay, and what side are you on I? Think that I should get the retirement. I was promised for 26 years.  I'm a para educator Okay, okay So and I don't have a problem with them changing the program, but I don't think they should change my program I see I think if the new people coming in you want to change theirs. I'm okay with that I get it. So the plan that you already agreed to right being retroactively Modified yeah by this policy and you think I didn't sign up for this deal. That's right I put in all these years for this deal. Why are you changing the rules and I do believe in public education? Okay, and I do believe contrary to what has been said that the majority of us really do care about our kids and we care that they get a good education and I think the majority of teachers at my school do the best they can okay and put in a hundred and ten percent all the Time I had public education. What would you say is the advantage? You probably have a different perspective than I do. Well, actually, I don't know. I think the advantages are Diversity okay Because I believe I grew up in the 60s and 70s and I went to an all-white Predominantly Christian Catholic public school same thing in middle school and I personally think that our world has changed considerably for the good and I think young people Should go to school with people of color with people of different religions Okay, of people of different ethnic backgrounds, and I think it makes our young people better Why do you think that makes people better to be around because I think it makes them see? Everything differently, and I think it starts with our young people my father Was pretty set in his ways, you know, he felt a certain way about Muslims and Different things like that, and I know I couldn't change it. There was no way to change him Okay, he was thinking close-minded you can change our young people And if you put them in a setting where they're all together and they look at a person as being You know Susie instead of Muslim, right? It changes things for the better. Absolutely. It's like a social education Along with everything else and like an hour here in Lexington I mean we have Lexington Christian Academy, which is obviously very Christian school and we have Lexington Catholic But I see the problem with those schools is that They're predominantly Christian obviously and so those kids never get to be around people that are Hindu that are Buddhist that are Muslim that are Jewish. Why do you think the schools who are predominantly Christian? Prefer not to have, you know, other religions in their school system because that's not their belief And they don't they wouldn't want their kids to maybe listen or pay attention to some other views And maybe not stay in the Christian faith. Do you think it's important for someone to question what they believe to be true? Even at a young age. Oh, yeah, okay. I think it's important to question just about everything. I think so too And I think and it's also by asking people about themselves That that's where you learn more Because if you only see things your side and never listen to another side, yes You then stay in that same little narrow mind and it doesn't mean that you'll always change your mind But I like having friends that are different. Okay, because it just gives you the opportunity to like Bounce your ideas on different sides. Absolutely. Can I ask one weird question? Sure. Go ahead Assume one of your friends was right and I don't and they have a different belief than what you have, right? Would you be more inclined to change your beliefs to whatever they have if they can present you with a reasonable argument for? Probably so probably although I'm pretty set in my views I can be made to see another side Okay, and I think that that's more so of young people. I think so because I don't think there is set in their ways as the rest of us are They're not and you know, I think it's good for our young people to hear other and I'm glad we had that I think so too. I think school could be a real and what was it? I had a few years ago We do a big board of the world Okay, and then we put pens on it Where some of our students are from because we're lucky to have kids from all over the world You're absolutely year. I messed up was I didn't go find our little girl from Siberia Okay, when I was growing up we always told that they sent the bad people to Siberia And I wanted to see you know one of those bad people from Siberia. Okay, I doubt she'd see the humor in it But you know all right one last final weird question that sure what's the good way to learn true things? Is it I know you can be around different people with different ideas, right? Is that always a reliable way to come to a true conclusion and not always? What is a good way to come? There's one way? What's the best way? Ask around You know now we have a wonderful thing called computers. We do got computers and you can do a lot of really cool And you know enter just investigating different topics. Yeah computers fine. Have you ever seen quantum leap? I love that show. Yes. I have That's yes, that's an old show. I had to tell you that's like that's my age rainbow quantum leap and experience a stargate or something like that original Star Trek, right, but uh There would be a guy named or there would be a computer called Ziggy That the guy would foam right knows like this thing back then they didn't understand what they were alluding to because they were just like Some it's some future science. Yeah worry. It has the news has newspapers has music on it Right that thing will never exist. Just pretend what it is But now we have that like these are what those are in a sense smartphones absolutely So we all have access to that so well, there's even there was an old cartoon called the Jetsons. I remember the Jetsons a Lot of what they had is true today.  I mean there's so much stuff now Dinner Those were like Mind-blowing and the jelly bellies that like oh, I'm eating toast right so isn't it But I don't know if they're better or not, I don't know if the phone has made anybody better or not I'm sorry the cell phone. Do you know cell phones are made better because a lot of my students they don't Develop real relationships where they have friends. It's all done. I think it's different But it doesn't take away the opportunity to still make friends Which is also its own thing, but this is just another avenue to have right keep in touch with people and I think Nothing set substituting each other. They're just supplementing each other and in that sense. I'm totally fine with it I still think you have to do it through the effort like make friends one-on-one So I grew up the old way where you had friends you played outside. Yeah, they long I'm not with your friends in here, you know the phone was my parents thing You know we didn't get to use I didn't get to use the phone Okay, even then it was just to make plans and get out. I used to have Video games that we're not connected to the Internet. Oh, if I wanted people to play with me I have a lot of friends with neighbors and write them to my house Right, my mom had to know who they were and we just sit and play But we'd have we'd be sitting right next to each other and now that made a difference Now I can play with thousands and my son does that and while it's convenient


for tomorrow but I'm starting in for Justin who's always sick. It's my pleasure to introduce Ben Sighetti from Microsoft and tell you a little bit about his background which goes on for pages and pages and pages which is phenomenal because with a background from aerospace missions with NASA all the way down to like IoT devices, fitness trackers, doing analytics, data analytics and things like that all the way now to solutions on Hadoop and now with Microsoft he's going to be talking to us about the Microsoft Cognitive toolkit. Good thank you very much. Kind introduction. I might be okay. Anyone here okay? I'd rather not use the mic. I use it for the recording. So right the Microsoft Cognitive toolkit this is a relatively new deep learning framework that has been open sourced over a year now came out of Microsoft research and it's already in production a variety of different Microsoft tools and services. So I'll just give you a background on how Microsoft's dealing with deep learning today. It's pretty much along a variety of whole portfolio of different products right? One thing that I might touch on later on in the period of time is the cognitive services that are available. These are pre-built deep learning models focused on computer vision, speech, NLP that are really available as web services accessed via RESTful APIs right? Everything done here was using these what we call CNTK rules it's formerly called CNTK as the cognitive toolkit. Skype translator itself, a lot of deep learning in the background there. Cortana the personal assistant. If you're not familiar with Siri and Alexa or the same vein deep learning back there. The Bing team uses deep learning extensively for search, for relevance and tolerance. This is again another very new product out of Microsoft's augmented reality tool if you've seen it you know what I'm talking about. All of this is coming out of the two Microsoft research teams, one in Beijing and the other in Boston. So I briefly mentioned the cognitive services, a couple screenshots of a few apps that were made a year ago on the left is Howl.net. If you guys remember it kind of went viral when it first came out. It's just a little app, a little HTML, I think the whole thing was like 50 lines of HTML and JS and it tapped into one of these cognitive services, the APIs. And it pretty much just passed an image and it would predict, it would first actually affect faces and from there make a prediction on the individual's gender and age. Second one, the middle one here is a captions bot, similar to, again part of the cognitive services, you just feed an image and respond back with a sentence, what it thinks it's actually saying. Thirdly is a prototype that was put together by one of our partners, this is Lieber. They're a refrigerator manufacturer and they started putting cameras within their refrigerators and pretty much detecting all the objects that you see to do pretty good with pretty good activity.  And it's a competition, it's been going on for a while, internally across a variety of different academic groups and other organizations that are pretty much trying to get to classify what they see in these images with as low as an error rate as possible, right? And in 2015 Microsoft actually hit, pretty much got the lowest error rate, low under 4%, which was on record all the time, right? Big jump from, as I said previously, where Google excelled. And so that was on the vision side, on the audio side, there's now a speech recognition, another sort of milestone here that's been done is being able to recognize just audio and be able to transcribe this with an error rate of 6%. And that's practically where humans actually start messing up. So we're matching human error rates in terms of speech recognition. And all of this, everything I've mentioned here so far, all these different use cases, research papers, competitions were all done with this, the cognitive toolkit, formerly called CNTK. You still see it at CNTK, GitHub at CNTK. So it's really easy to use, it's meant to be just something that you can come in, define a few layers for your neural network and not be bothered with the underlying infrastructure. It comes with a pretty rich set of tools, a library already built in, so this includes feedforward neural networks, convolution, recurrent networks, long short-term models, deep structured semantic models, sequence of sequence models, and this goes on. It's optimized for GPUs, you can still use on CPUs if you like, but really GPUs is the real intention. And not just a single GPU or one server's worth of GPUs, but this goes across a farm of servers powered by GPUs. This is the, so the parallelization is actually done with using the MPI protocol. And in terms of APIs, the whole library itself is a C++ library, because it's a source code C++. There is an internal language called BrainScript, which you can use, but now there are APIs available for C-sharp.net, of course C++, and Python. This v2.0 provided Python wrappers. It's built for Windows and Linux, so internally we actually did Microsoft team, so they might be using Windows for their development, but for actual big jobs, they throw it on a big Linux cluster. Right, so that's just how it's done, and it's been, of course, an open source, it's been active, the developers have been active on there since January of 2016, it's all on GitHub, so yeah, everything is available. And by the way, this is a tool where the internal and the external are tri-identical, so we're using the GitHub master, or branch perhaps, but it's all on GitHub, right? Okay, so a few things. A few things to point out, two important notes here. One, it's that you can build an arbitrary neural network. Okay, so you can actually define different layers, different types, using this framework. So you can say, and this is what's actually quite novel, innovative about this framework compared to some other ones like Torch, TensorFlow, MXNet, what else, you know? So here you can actually say, okay, I want a neural network where the first layer is dense, second one is convolution, third is recurrent, then followed by six dense ones and such and such. And you can actually stack them up as you wish. So you can get really creative on the architecture that you put together. And of course, try all these various permutations and see which ones really work best for you. There's a big gallery actually on the site, which I'll share in the end, of what different architectures that some have tried and were successful with. So you can take a look. And it's production ready. Like I said, it's used internally on a variety of different Microsoft solutions already, right? So it's true and tested from the production side. Of course, there's got C++. So any model that you build, you can just print production. There's a BLL you call from C++ and there you go. Imagine your classification regression. So it comes in sort of three steps. There's a reader, so depending on whether you're processing text, audio, video, there's a set of different readers available for the data ingestion. There's, of course, a network that you build, the multi-layered system, the architecture there. As I mentioned, again, it's optimized for GPUs, but CPUs work just fine. So you can just install your laptop and play around if you like. And then for the training part, the various different flavors of stochastic gradient descent. Because we are talking about three large data sets. So it has to be some stochastic functionality. So talking about building arbitrary networks. So here I'll give you a very simple example of a two-layered feed forwards or a dense system. So here, X is here, is our input, that's our features. There's a weights matrix, followed by I've added to a, and then I'll put in addition of a bias. That is our first layer, very straightforward. And again, it's just matrix multiplication, and that's why GPUs come to play. That sigma there is a low provision for the sigmoid function. For those of you who are familiar, it's a little mapping of the real domain to the unit range. Softmax is similar in function. And then the actual training, the criteria is cross entropy. That's a pretty standard approach. So here, the actual code, this is now in brain script. It looks like this. In Python, it looks very, very similar. Again, I'll give you some links on some code, some actual tutorials. Well, that's it. Two-layered network, plus the loss function that I defined, and I'm ready to go. So we just defined our whole network. I think of course, build these as long as you like, layer them on. You can bunch layers together and reuse these bunches. So you can go macro in terms of layers. In the Python API, I can actually put a for loop in, say, for I to 1 to 6, give me events layers, and then go. So really, really straightforward. And in terms of actually what the model looks like, so it is always a graph built. And this is something that's editable. So this is, in a sense, it's executed after the graph is built. So you can actually just hold on, edit the graph and then execute. So everything's editable. So here you see the weights. That's the matrix modification piece. And the bias is coming in every step. You can go and change those. You can bring in, so it's recurrent. You can actually bring in the previous steps. T minus 1, that sort of thing. So it's all available here. Pretty straightforward. And some, a few things that are, that's actually really important here is automatic differentiation. So for stochastic gradient descent, as you know, that's the gradient. There's the differential there. And here you're actually to go and define the differential. You just pass it your loss function and it will just too much go and auto differentiate for you. Yeah, big deal. And everything's cloneable.  In terms of different layers, this is actually something I screenshot it from the GitHub 30 page. So it just goes through some of the different layers that are currently available. Dense, convolution, max pooling, everything goes on and on. So you can just play with these layers. So getting to something a little more fun, actually a little demo. It's talked about a common computer vision task, which is classification or more importantly object detection. So the example I gave earlier for the faces or all the different little products within the refrigerator. This would fall under this object detection category.  This is something that was developed with some work that was done by Microsoft Resilient in conjunction with the security field firm Alcatra. So they went ahead and they were pretty a lot of video detection tools. I hope the video goes here. Let's see. So first one, I think it's a spatial. There you go. So now this is actually a spatial recognition. It's fine even. So this is compared to a class that's still brought in and still recognized. And of course the match mentioned what's detected on the sunglass. Here's the hope, yeah. It'll actually identify you at some ender mask. So again this is joint product with the security firm. So there's actual use case that sets out there, right? And all of this would be put into the production. Here's a stocking. As ladies weren't stocking there. It's classifying all this pretty correctly. And it's an alert mechanism for when the fellow's coming in with a helmet. Again, it actually recognizes the individual. This one's pretty funny too. I think it's funny because it's a really cool set here. So right after the mask is all detected. Yeah, actually right multiple people, right multiple guests. So this is vehicle tracking. So here there is some space where actually the vehicle types are classified. And there's a velocity and color prediction made as well, right? So that's pretty neat against school. And by the way, everything's real-time, right? Everything we've seen was real-time. So this meant to be real-time. So yeah, event detection. So here you see, okay, so it's a little busy sort of unwrapped. There's a fellow actually crossing. And this actually gets tagged as a funny event. As a warning, right? Because that fellow is another piece.  So yeah, that's how it was. So they're about to cross the highway. Again, that event is about to be tagged as... Right?  They're around here. So yeah, it gets tagged. Here's a similar one in a couple more. And this is a real concern, right? A real risk, yes. This is a real concern. Again, yeah, so all these events get flagged. This actually employs the city. Comes in pretty ballsy. Fast-forward a little bit. Give her a time, my friend. Yeah, helmet detection. I'm going to try and skip, maybe go through this. You already saw something similar. So again, depending on the scenario, if someone has come to the helmet, you want to tag that, send out a warning that some person shouldn't be walking around in the ways of the helmet. No need for that here. I'm going to go halfway through. I just want to show one last piece, which is abandoned object detection. Yeah, so the still frame first, the controls defined, when someone comes in and just leaves an object. There's a timer on there, and after a few seconds or something, they'll get flagged as an abandoned object. So it's ideal for any sort of public transportation, sort of a public area, right? If something's abandoned, you want to flag it immediately. Or another similar one. This one is where it tries to differentiate between, similar to the first where there was traffic and humans were caught in it, similar scenario. So flags, humans as alerts, but then when cars combine, those just go through as unsexuals. Okay, so let me go continue. So this was done, I think when there was a matter of three, four months, that these folks put this together, they already had the data sort of assembled, so the tough part in the sense was done, the data collection. And they just went ahead, started feeding this to a cluster of GPUs, using CNTK and built out a variety of different models. Each of those use cases had their own specific models. So currently there are quite a few, everything's on GitHub, including a variety of different tutorials, and using different APIs. There are even Jupyter notebooks available, so you can actually just spin up Jupyter, all the documentation's there, and just walk through these tutorials. Great resources. There's also Azure notebooks. Again, Jupyter notebooks hosted on Microsoft's cloud. These, by the way, are currently free. You can do your own Azure notebook, and there's a nice CNTK tutorial there as well. But more importantly, getting the actual use of GPUs. So on Azure right now we have GPU-powered VMs. These are NVIDIA GPUs, so they scale from an instance for one, two, and four GPUs on the same VM. So these can be used for anything, but in this case there's actually an image built by our algorithms data science team used internally, and then shared publicly with everyone else, which comes pre-installed and pre-configured with a variety of different machine learning tools, including deep learning frameworks. So if you see the second paragraph there, MXNAT, CNTK, TensorFlow, Keras. So everything is pre-configured, two on one on these GPUs, and you can just spin one of these things up. All the software's free. And yeah, just get busy during your deep learning work. A few links. CNTK.ai is the actual website. So as I mentioned, there are quite a few of the gallery of architectures out there. So what others have tried, where they've been actually really, really powerful, what's really worked for them. So you can just browse those and just clone it pretty much and go forward. And everything else, right? All the issues, the big wiki there too. And of course, all requests are very much welcome. So please, if you get involved, feel free to contribute. And that's it, actually. That's my brief little talk on CNTK. Thank you. Questions. Our writing, so this has been asked for that. It's in the works. I don't have a timeframe for you. So right now it's brain script, c-shop.net, c++, and Python. So the real time one is actually pretty easy. So that is, it's computationally, of course, not as expensive as a training bit, right? So you can have these, once you have the module that you generated, you could run them on CPUs. You don't necessarily even need the GPUs for the prediction part for the operation. So you could potentially actually have this on a relatively, you know, edge device, a relatively weak processor.


Welcome traders to another Tick Mill earning season preview with me Patrick Munley before we jump into today's presentation As always want to adhere to the risk disclaimer and most important To the content today is the fact that the views and opinions expressed by me are solely mine They're not indicative or representative of those held by Tick Mill UK or Tick Mill Europe Limitids. Okay, let's jump into today's report We're looking at Microsoft. Microsoft are set to announce earnings after the close of New York trade-in today Consensus is for an earnings per share of two dollars thirty on revenue of fifty point zero eight billion Should note there is a whisper number out there that the earnings per share could come in slightly higher at two dollars thirty two cents In terms of the earnings report, obviously we've had news this week or sorry last week that Microsoft intends to lay off 1,000 workers across multiple divisions with fears of recession looming Axios and business inside a recent report these layoffs could come in Under 1,000 workers, but will span various regions and departments Microsoft spokesperson has confirmed the accuracy of these reports in email software giants first-quarter earnings also come amid foreign exchange Pressure and concerns about weakness in the company's PC business After a pandemic-related boom the PC market is in its sharpest fall since data on it was first collected in the mid 90s according to analysts Microsoft cloud revenue accounted for almost 50% of the company's total revenue during its fiscal Fourth quarter results in July and grew 28% compared with the prior years quarter Despite foreign exchange and PC headwinds the software sector in general is expected to be one of the highlights of the tech earning season Microsoft's second quarter forecast however will be crucial in recent quarters Microsoft sock has fallen even after the company reported strong numbers Then flipped to the green after the software maker delivers its forecast roughly two hours later Despite Microsoft's fiscal fourth quarter earnings miss in July the company's guidance sent the stock higher shares of Microsoft have fallen 29.78% in 2022 outpacing S&P 500 decline of 23.9%. Let's take a look at some of the stock trading patterns around the Microsoft earnings release Microsoft shares have moved higher in the immediate Aftermarth of earnings eight out of 12 previous reports on average stops moved up 1% in the first day of trading after the company's earnings release based on the previous 12 earnings releases Microsoft is more likely to trade higher one day after earnings from average gain of 0.4% On average the stocks move higher by 3% one week after earnings in terms of volatility What we can expect there options traders are pricing in a 5.4% move on earnings Stocks actually average a 3.1% move in recent quarters and from a flow and sentiment perspective Noteworthy here that there's been just under 8,000 contracts Bid on the $250 call expiring this Friday and options order flow sentiment in general is bullish Analyst sentiment going into the company's earnings release has 89.3% of coverage bullish Microsoft's share price has drifted down 8.1% post its prior earnings release Using the last 12 quarters of the data the average earnings drift as after announcements is about 5.6% Let's pull up the Amazon charts and see if we can identify any near-term trading opportunities In the stock so from a technical perspective We have seen a decent recovery in line with broader risk sentiment in terms of the market So whilst we hold the $234.25 as the swing low I have a technical upside objective at $258.78 remember there's a decent bid in the market for those 250 calls So what I'd be looking for is any close now through The $249.55 area to engage on the long side targeting that $258 We do have a gap above there at $266.50 In terms of a stop on this opportunity, I've been placing a stop level just below the $245.00 area But the setup looks constructive here at the moment obviously any loss of the $234.00 on the downside will be bearish development to my mind opening a retested price cycle lows $219.00 And just below there on the weekly timeframe we have $214.53 as the high volume load for that post-pandemic advance and the 2022 pullback because that's really a crucial area to hold If we fail at $214.00 the next downside objective is going to be an equality target to the downside $190.70 but for now constructive looking for longs through that $249.50 As always traders plan the trade, trade the plan and most importantly manage your risk Until next time, thanks very much


Russia's threat to the Baltic countries is increasing. Russia does not give up its attempts to gain control over the Baltic countries. Yahoo News has obtained confidential strategy documents drawn up by the Kremlin that reveal Russia's ambitious plans to exert its influence in the Baltic states of Estonia, Latvia and Lithuania. Formerly annexed states of the Soviet Union, all three are now proud NATO and EU members with governments fiercely critical of Russia's foreign policy. Among other things, the documents show Moscow's intention to thwart the construction of NATO military bases, weaken the local democratic governments, expand Russian cultural and linguistic and bolster pro-Kremlin political organisations. But in a running theme throughout the documents, which were drafted in 2021, Russia's subsequent invasion of Ukraine has undermined almost every aspect of the plans. Estonia, our allies and partners, have long been aware of Russia's interest to use hybrid tactics to pursue their goals and try to assert their influence in other countries. Estonian Prime Minister Kaja Kalas told Yahoo News, It's Russia's classic playbook. Many of their tactics are taken straight from the KGB. We know our neighbour. She continued, Our job is to continuously share our knowledge of Russia's actions, build resilience and stand against Russia's direct and indirect attempts of influence. We have had to bolster our defences to stand up against any threat, including hybrid threats. And it's also crucial to discuss these matters publicly. In Greta Simonite, the Lithuanian Prime Minister similarly told Yahoo News after reading the Russian plan for her country that it was all something you would usually suspect. Latvia has been facing Russia's desire to strengthen and expand its influence in neighbouring countries since the restoration of independence. Said Kristianis Karins, the Latvian Prime Minister, Latvia maintains a realistic view of Russia's foreign policy and its initiatives. Latvia actively advocates for and promotes Russia's isolation. The documents were drafted by the Russian Presidential Administration's Directorate for Cross-Border Cooperation, which formulated similar plans previously revealed by Yahoo News for the creeping annexation of Belarus and the western toppling of Moldova's pro-western government.


Our first definition is just to deal with the word heredity. And all that is is passing traits from parents to offspring. That's super straightforward. What is not straightforward is what is a trait? Oh, guess what our second vocabulary word is? A trait is an observable characteristic. Dude, can we get any more vague? Heredity is passing observable characteristics from parent to offspring. Seems pretty straightforward. Has a lot to do with DNA. We have more words. Traits can come in flavors. If we have like a trait is a big picture, like broad generalized character, like eye color, height, hair curl, melanin production. These are all things that are traits. They're broad, they're general, but there are specific forms of the traits. And so your phenotype is the actual expression of a characteristic. So for example, the trait, the eye color trait has multiple possible phenotypes based on the genes that the person has. So one possible phenotype is blue eyes. Another possible phenotype is green eyes. Another possible phenotype is brown eyes. The phenotype can have much variation, but it's the actual physical expression of the trait in an individual. We can describe the phenotype of critters. Well, I hope that it isn't a huge leap to say, well, what causes a phenotype? There's got to be some link to DNA here. And in fact, the genotype is the alleles that code for a person or a critter's physical traits. And when I say the alleles that code for this, we talked about alleles are just forms of a gene. Probably the one word that we're going to use more than any other in the course of the next three lectures is allele. Because the different forms of the gene are the things that we're keeping track of when we make heritable crosses. When Homeboy Mendel was doing his baby pee making crosses, he would look at the traits, the phenotypes of his parent pee plants. And then he would predict, if I cross these two parents and they have these phenotypes, what are their babies going to look like? And then he used that to try to figure out, well, how is that happening? What is the thing that these parents are passing along the alleles that they have that code for these forms of genes? Genotypes. We're going to have so many opportunities to mess around with this. So be really patient if you're feeling like, ah, this is confusing. Be patient. Genotypes are always two alleles. Okay. In diploid critters, genotypes are always two alleles. And we're only going to deal with heredity in diploid critters. And that's because, dude, we do not want to try to keep track of more than two alleles. The alleles can be the same allele. Why do we have two? One came from the sperm parent. One came from the egg parent. When we make sperm and eggs of our own, they get one of our alleles. All of my eggs have one copy of alleles that are in my diploid cells. That's meiosis. We understand the process of meiosis. Genotypes can be homozygous, is two copies of the same allele, or heterozygous, two different alleles. All genes have two alleles in their genotype. Because they're diploid critters. Because you have two copies of every gene in your genome. With the exception of Xs and Ys, which we'll talk about in the next lecture. Okay. I, oh, one more thing. Ah, okay. We can have different alleles. And I need to give you a couple of words. These are familiar words, I think. Alleles can be dominant, or, oh, I'm sorry, I have to take it over here, recessive. I ran out of room. A dominant allele will be expressed, even in a heterozygous genotype. So a dominant allele, if that dominant allele is present, it will be expressed, even if there's only one copy of it. Recessive allele will only be expressed if there are two copies of it. Okay. How do you feel about that? Do you feel like, what is she talking about? If you do, take a deep breath, because we're about to go in and look at Mendel's peas. He found seven characteristics, describing seven traits, describing his peas. We're going to look at each one of those seven traits. Each trait had two alleles. We're going to look at what those two alleles are. And one allele was dominant, and one allele was recessive. So we're going to look at the dominant and recessive alleles, which I only tell you that to say we're going to have lots of practice with this vocabulary and something actual physical to talk about. Okay. Onward to pea plants.