Hi, it's Zor. Welcome to Unizor Education. Today we will continue talking about mass plus and problems, which is part of the Unizor.com. Now this course mass plus and problems contains different problems, which are not really standard, just to check your theoretical knowledge. They are a little bit about thinking. Non-standard, if you wish, non-typical. So today we will talk about averages. I do recommend you to watch this lecture from the Unizor.com because every lecture, including this one, contains textual part, notes, basically, which is like a textbook. And in general, all the lectures are logically related. There are menus, so the site is, by the way, absolutely free. There are prerequisite courses here in this particular case. The prerequisite course would be mass for teens. There's also physics for teens on the same website. All totally free, no ads, no signing required. I mean, if you wish you can. So just very good source of information. Okay, so, talking about averages. Well, first of all, there are many different types of averages. For example, everybody knows something like a arithmetic average or mean. Let's say you have number four and number nine. So you basically add them together and divide it by two, and that would be 13, like 6.5. That's a arithmetic average. There is a geometric average, which is the square root of their product, which is what, 366. Well, 6.5 is in between. Six is also in between, so it's different averages. There is a quadratic average, which means you have to put four square plus nine square divided by two and square root. That's what, 16, 81, 97 divided by two, 48 and a half, almost 49, so it's approximately seven. Also in between, but it's different averages. Now, what's interesting is that arithmetic average is always greater or equal to geometric average. And that's what we are going to prove today. And not only for two numbers, but for any number of numbers. So, now, here is a set of numbers where index i is changing from one to n. Now, a arithmetic average is, you have to summarize them, and divide by number n. That's a arithmetic average, and I call it a, and a stands for arithmetic. Now, geometric average, let's use the letter G. That's, you have to multiply them, all of them. So, it's a product. It's a Greek letter pi, also from one to n, x i. And have the root of n's degree, which is one over n power, as you know. Okay, so what we have to really prove is this. Okay. Now, there are many different proofs. It's not really like an easy thing, by the way. So, there are many different proofs. I will present two. And one of them, the first one which I'm presenting is by a famous mathematician French, I believe, Cauchy. Okay. It's almost like by induction, but not exactly. So, first, we check it, obviously. That's the approach which we're using all this to check for induction, to prove by induction. So, for n is equal to 1, that's obviously equal to, it's only one number. So, it's, some would be that number itself, x first, divided by 1. So, it's still x 1, multiply x 1 from i is equal to 1 to 1, basically. So, it's also x 1. And the square root of the first degree, which is also x 1. So, it's x 1 and this is x 1. So, it's equal. So, that's equal. Now, with 2, it's just a little bit more difficult. But still, not very difficult to prove. With 2, I will do something which is, I believe, general kind of a logical approach. Here it is. So, you have, let's say, two numbers, x 1 and x 2. So, their arithmetic average is this. Now, their geometric average is this. I use square root. Now, I didn't mention it before, but we are talking only about non-negative numbers. Because otherwise, square root would have some problems, etc. So, it's real non-negative numbers. Now, I would like to prove that this is greater than this. Regardless of what kind of non-negative real numbers x 1 and x 2 are. Now, I will prove it the following way. I will start from something which we have to prove. This is some kind of analysis stage. I will make certain transformations which are invariant and reversible. And obviously, what's irreversible? Well, for pluses minus, for square root, it's power. And for division, it's multiplication, vice versa. So, in many cases, these are reversible transformations. For example, if we don't restrict ourselves to non-negative numbers. If we allow negative numbers, then square root and power of 2 are not really reversible. Because from minus 5, you can raise it to the second power of 2, that would be 25. But if you will extract square root, arithmetic square root, you will have 5, not minus 5. But within the non-negative numbers, that's a reversible transformation. So, I will use reversible transformations to come up with absolutely true statement. Is it a proof? No. Because from a false, we can always get to true using logical transformations. So, the proof is from absolutely true statement, logically come up with whatever we need. So, in this case, I will just reverse all the transformations. And that's the proper way of proving certain things. So, how do I do it? Well, I will square it, the both parts. So, I will have x1 plus x2 square divided by 4. And I will try to prove it that this is x1 times x2. Or x1 square plus 2x1x2 plus x2 square greater than 4x1x2. 4 goes to the left part. I will have x1 square minus 2x1x2 plus x2 square greater than 0. Now, this is obviously x1 minus x2 square greater than 0. Now, this is absolutely true statement. x1 minus 2, since it's square, is greater or equal to 0. Now, I'm saying all the transformations which I was just making are reversible invariant transformations. From here, from the absolutely true statement, I can go to here. From here, I can go to here by adding 4x1x2 to both sides of equation. That's an invariant transformation. Now, this I will transform into this. Just, you know, change the explicit square into this and divide it by 4. And from this, by extracting square root from both sides, which is an invariant reversible transformation if we are talking about non-negative numbers, I get this. Now, this is the proof. So, analysis, you go from whatever needs to be proven to definitely a true statement. And then the real proof is to go backwards. And it's not obviously in these only cases, it's many other cases. That's the technique which you have to make, which you have to go through if you want to really prove the thing. Some people, for whatever reason, just stop at the very end of this. Start from this and come to this and say, okay, that's a proof. No, that's not a proof. Because it might be the false statement and you still can come up with a true statement. We did these examples, man. We were talking about logics in the mass-vegetance course, prerequisite course. Okay, so, for one, we have checked, for two, we have proven. For n equals 2. n equals 1. We check n equals 2, we prove. The proof is not exactly the way how we do it in, I would say, typical inversion. Induction, sorry, induction. We were usually going from n to n plus 1. So, from 2, we can go to 3, from 3 to 4, et cetera, et cetera. Now, we will do it differently. We will do it from n to 2n. So, I assume that for n, it's true. An greater than gn. Arithmetic average of n numbers is greater than equal to a geometric average. So, I assume that for n, it's true. Then I will prove that for 2n is also true. What it will give me? It will give me from 2, I can go to 4. From 4 to 8, from 8 to 16, from 16 to 32, et cetera. Only powers of 2, only if n is powers of 2, that would be the proof. Okay? So, that's not a complete proof. So, that would be a continuation. But the first thing I would like to prove is this one. So, again, my first stage, I check and prove for this. My second stage is this. And the third stage would be for all other n's. Okay? Now, how to prove it for this? Okay. So, if you have 2n numbers, let's divide it by two subsets. Okay? Subset number one would be xi where i is from 1 to n. And subset number two would be all xi's where i belongs to from n plus 1 to 2n. So, I have divided my group of 2n numbers into two groups. From 1 to n and from n plus 1 to 2, 2 to n. Now, each one of them has n elements. So, for each one of them, this is true. So, a1n greater than g1. Now, one means for the first subset. Now, for the second subset, I have a2n greater than g2n. Okay? Now, what is a2n, which is arithmetic average of all of them? What is this? Well, that's sum of these and sum of these. And then you sum them together and divide by 2n, right? So, what is the sum of these? Well, if I know the average, if I average multiply by n, that will be sum of these, right? n times a n first. Now, sum of these are, they're also number n times a n 2. And then I divide it by 2n, right? So, this is a n 1 plus a n 2 divided by 2. Okay. Now, what is their geometric average? gn, no, g2n. g2n is their product in power 1 over 2n, right? So, what is their product? Well, product of the first one, since we know geometric average, that would be geometric average of the first one. And I will raise it to the power of n, right? So, this is product in the power of 1 over n. But if I will multiply, if I will raise it to the power of n, I will get the product itself. Then I have to multiply it by another of the second group, power of n. And all together, I have to raise to the power 1 over 2n. So, this is a product of the first n of the first group. This is the product of the second to the power of n. And then, from all the product of all the numbers, I have to raise to power 1 over 2n, or root of the 2n's degree of power. Okay, which is equal to what? It's equal to gn in the power of my rate, times gn, the second one, in the power of 1 over 2. Powers are multiplied, right? Which is gn times gn to the power of 1 over 2. Now, okay, that's done. So, I have my expression for g2n, and I have my expression for a2n. Now, the rest I can wipe out. So, this is sufficient. Now, a2n is equal to an first plus an second divided by 2. So, all the 2n's is basically average of two different groups. Now, this is greater than or equal to, I assume that for n, my inequality is true. So, this is greater than gn first plus gn second divided by 2. I just replace this with this, and this with this. That's why I'm putting the greater than sign based on this. Now, these are two numbers divided by 2, which is arithmetic average of two numbers. It's greater than geometric average of these two numbers, which is what? gn first times gn second square root. But this is exactly this, equals to g2n. And that's how we have proven that a2n is greater than g, or equal to g2n. So, arithmetic average of 2n numbers is greater or equal to geometric average of these two numbers, but under assumption that for n, it's true. So, now, since we know that for n equals 2, it's already true we have proven it. So, from n equals 2, we go to n equals 4, from 4 to 8, from 8 to 16, to 32, etc. So, we have proven for all numbers n. So, for all n is equal to 2 to the power of k, where k is some natural number, we have proven it. Now, how to do in between? So, let's say my n is between 2 to the power of k1 and 2 to the power of k. So, it's somewhere in between. So, you have this xi, where i from 1 to n, and n is here. So, what I will do is, I will complement this set of n numbers to a set of, let's say, m is equal to 2 to the power of k minus n. This is number of elements which I need to complement to 2k. So, I will put, let's say, g n m times. So, my new set contains n given elements, and m, where m is this, g n, where g n is geometric average of these n numbers. So, all together I have n plus m, which is 2 to the power of k numbers in my set. So, x1, x2, etc., xn, and then g n, g n, g n, g m times, all together constitute a set of 2k to the power of k numbers, for which my previous proof has been already done. So, for 2 to the power of k, I know that everything is true, which means that x1 plus x2 plus, etc., plus xn, that's my sum, plus g m, and plus g n, plus, etc., g n. So, these are n elements, and these are m elements. I know that their arithmetic average, which is divided by 2, is greater than equal their geometric, which is what? Product of all of them, which is product for x i, i from 1 to n, times product of all my g n, the m, the m of them, so it's the power of m, and then I have to have power 1 over m plus n, right? m plus n. So, let me just write it again in a more compact form. So, this I have to divide by, well, I don't know why I put 2 here, I have to divide it by m plus n, right? All right. So, what do I have now? I have sum of x i, i from 1 to n, plus m times g n. This is the left part, and all of this is divided by 1 over m plus n, right? So, that's my left part, and I know this is greater than or equal to product from 1 to n of x i, times g n to the power of m, and power 1 over m plus n. Okay, that's good. So, this is a n, and this, sorry, this is a m plus n, and this is g m plus n. Okay. Now, let me just simplify it. What is this sum? Well, this sum is basically the average times number of elements. So, it's a n times n. That's this one, plus g n times n. So, that's my left part, right? Divided by m plus n. On the right, I have... Now, what is this product? Product is g n to the power of n, times g n to the power of m, and all of this is power f plus 1 over f plus n. So, that's how my inequality looks now, and this is a true inequality because it contains m plus n to the power of k elements. And basically from here, I will derive whatever I need. Okay. How do I do it? Now, what is this? This is g n power of n and power of m. So, together it's m plus n, and then 1 over m plus n. So, this is just g n on the right. So, on the right, I have g n. No. m plus n and 1 over m plus n. On the left, I have this. a n times n plus g n times n. And I will put this m plus n to the right side. So, what's left? g m, g m. So, what's left would be a n times n, greater than g n times n, because g n times n will cancel out, cancel this, and I have a n greater than g n. Arithmetic average greater than... Now, I can actually do exactly the same by complimenting, not with geometric average, to complete my set to 2 to the power of k elements. I can use the arithmetic average and do exactly similar kind of things and get the similar result. I put it in notes for this lecture. So, that's probably would be a nice exercise for you when you will read it. It's basically the same kind of proof, but slightly different. And then I wanted to have another proof very quickly, which belongs to Podia. Very clever, I would say. Now, you know that this is e to the power of x graph. It has tangential line at x is equal to 0 at 45 degrees. So, the tangential line equation would be y is equal to x plus 1, obviously. So, it's x raised by 1 up. So, if I will use... If I will shift all the graphs to the right, I will have... Instead of this point, I will have this point. And the tangential line would be at point x is equal to 0, 0. Not 1, 1. Here, 1, 1. That's tangential point. 0, 1. We shift to the right. Now, when you shift to the right, the graph of the e to the power of x would be e to the power... Sorry. e to the power x minus 1. And instead of y is equal to x plus 1, I would have is equal to x. Now, I can actually spend some time talking about the following equation. Now, from the graphical standpoint, you obviously see that e to the power x minus 1 is greater or equal than x, right? This is greater than this. It's above. Now, we can actually go to some calculus thing and saying, okay, the first derivative at this point is equal to 0 between the difference of these two functions. And it never actually goes to 0 anywhere else. It's always positive. So, if I will have difference between two functions, and that means that it's convex, and that means that it's always positive. If it's 0 at one point and it goes only up. But it's kind of obvious right now just from the graphical standpoint. And they don't want to go into these calculations. So, I will take this as basically as granted and obvious inequality. But from here, it's very easy to have our inequality because I will use it for x i divided by a n. So, what will be? I will have e to the power x i divided by a n minus 1 greater or equal to x i divided by a i. So, instead of x, I'm using this. Then, I multiply it from 1 to n. What will I have? I will have e to the power of x 1 divided by a n minus 1 minus e to the power for x 2, for x 3, etc. And powers are aiding together. So, if I will add them together, I will have basically sum of x i divided by a n minus 1 would be n times. That would be on the left part. On the right part, I will have product of all x i's at 1 to n divided by a to the n to the power of n. Right? Because a n would be multiplied by each other. Now, what is this? Well, sum divided by n is a n. Sum divided by a n is n. So, n minus n would be 0. So, this is 1. That's why we have that 1 is greater than product of x i's divided by a n to the power of n. Or power of a n to the power of n greater than product of x i. And if I will use power 1 over n, I will have, on the left side, I will have a n. On the right side, I will have g n. That's the definition of the geometric average. So, from this very simple inequality, this greater than this, I can very easily derive this. That's a very clever actual idea. All right, so that's it. I suggest you to read the notes for this lecture. I put a little bit more, maybe, some logic behind these graphs, but it's obviously a simplest calculus problem, standard calculus problem, that this is greater or equal than this one. Other than that, everything was really kind of strong proof, two different proofs, that arithmetic average is greater than geometric average. And there are other proofs, if you want to. It's all brain exercise. So, I do recommend you to spend some time and get into this proof very, very close. So, you can read these notes for this lecture. To get to the notes, you have to go to unizord.com, choose the course mass plus and problems. This is algebra and this is algebra 2.0.2. That's it. Thank you very much and good luck.


Remember that an elementary matrix consists of applying a single elementary row operation to the identity matrix. And as long as we don't switch rows, this will always give us a triangular matrix. And as long as we work top to bottom, these will always be lower triangular matrices. For example, suppose we want to find the elementary matrix corresponding to multiplying the second row of a matrix by negative 4 and adding it to the third row. So we'll begin with an identity matrix with at least three rows and apply the same row operation to get our elementary matrix. Strictly speaking, we should limit ourselves to a single row operation at a time. In practice, we won't distinguish between true elementary matrices and matrices that either multiply two or more rows by a constant or add multiples of one row to other rows. However, to retain at least a vestige of the idea that these are elementary-ish matrices, we won't combine both of these into a single matrix. We won't do both sets of row operations at the same time. So for example, let's find the matrix corresponding to multiplying the first row by 3 and adding it to a second row and multiplying the first row by negative 1 and adding it to a third row. So here the important thing is that our working row is the same in both cases. It's the first row and it's affecting rows below it, rows 2 and 3. And we'll proceed in the same way. We'll begin with an identity matrix with at least three rows and apply the same row operations, 3 times the first row plus the second and minus 1 times the first row plus the third. And this leads to what's called the LU decomposition. Suppose we row reduce a matrix A to U. If we don't switch any rows and work top to bottom, the elementary operations correspond to lower triangular matrices. So we can write LA equals U. Now remember the row echelon form of a matrix is an upper triangular matrix and L will be a product of elementary operations that correspond to lower triangular matrices and remember the product of lower triangular matrices is also a lower triangular matrix. So we know that L is a lower triangular matrix and U is an upper triangular matrix. And now if I multiply by L inverse I can write A as L inverse U. But remember the inverse of a lower triangular matrix is also a lower triangular matrix. And this means we can write A as a product of a lower triangular matrix L inverse as an upper triangular matrix U. For example let's find a LU decomposition for this matrix and then let's find the determinant and the inverse. So we use elementary row operations to row reduce to an upper triangular matrix. So our first set of row operations might be and we'll apply the same row operations to the identity matrix to get a quasi elementary matrix which we then apply to our matrix A. The next step in our row reduction will be and we'll get a corresponding quasi elementary matrix which is then applied to our result. Now we can multiply these two lower triangular matrices to get another lower triangular matrix and so here we have a lower triangular matrix applied to our matrix giving us an upper triangular matrix. So our last step is multiplying by the inverse of the lower triangular matrix which will be and if we multiply both sides by the inverse we get where our matrix is now written as a product of a lower triangular matrix and an upper triangular matrix. And remember the value of writing this as a product of triangular matrices is that some things are much easier to compute. Since the determinant of a product is the product of the determinant we can find the determinant of our matrix as the product of the determinant of the two triangular matrices and since they're triangular the determinant is the product of the entries along the main diagonal. And so our determinant is how about the inverse? Remember the inverse of a product is the product of the inverses in the reverse order and since these are triangular matrices we can find those inverses pretty easily we find and so the inverse will be


Live from Las Vegas, Nevada, it's theCUBE. Covering IBM World of Watson 2016. Brought to you by IBM. Now, here are your hosts, John Furrier and Dave Vellante. Hey, welcome back everyone. We're live here in Las Vegas Mandalay Bay Convention Center with IBM World of Watson. This is Silicon Angles theCUBE, our flagship program where we go out to the events and extract the signal from the noise. I'm John Furrier, my co-host Dave Vellante, head of research at wikibond.com. Our next guest is Mary Glacken, who's the senior vice president of VP, senior vice president of science and forecast operations at the weather company. So, super excited. What a sense of a great job. I'll see that weather data we always geek out on. Certainly, we saw the catastrophe of the hurricane that could have been worse this past month. So, very, very timely conversation. Welcome to theCUBE. Thank you, thank you. Thrilled to be here. So obviously IBM is getting into the weather business. But you see it play out on internet of things. You're seeing it play out as an interesting acquisition because now weather factors into a lot of things. And something that's near indeed our heart is autonomous vehicles. Absolutely. Which is an area you don't really kind of correlate weather with cars. How does that relate? I mean, how does? Well, it has incredible relationships there. First, unfortunately, almost an extremely large percentage if I'm recalling correctly about 70% of accidents are weather related. So that's an attention gather right there. Seven zeros. Yes, seven zero. I think that's about right. I haven't looked at that recently. That's a big number. It's a big number. It's a big number. And we're excited about connected cars for connected vehicles really for a number of things. One is each of these vehicles gives us environmental information. So think about how your windshield wiper, how fast it's going, a whole set of that. We know something about how quickly the rain is falling. Think about every time your ABS thing engages there. We know something about the road surface there, whether it's wet or cold. We're obviously getting temperature information off of there. But what we want to do at the Weather Company IBM is what we're already doing in aviation is treating these vehicles as a two-way street. Extract that environmental information off of them and then provide them back a precise, accurate forecast. So in aviation today, we do this for turbulence forecasting. We take information off of the airplane and with our partner, Go-Go, we provide them back a turbulence alert. So as the planes are flying down, you can warn everybody behind you like that. So I think the internet of things. The trade-off on my Wi-Fi for checking email and streaming movies for a more comfortable ride. Absolutely. A safer ride, especially for those people that don't keep their seatbelt on. Exactly. Unless we're a terrible. I couldn't resist. I'm always complaining about how slow the Wi-Fi is. Then it's satellite-based. So it's going to land, it's going to ground. Okay, so that's really using precise measurement of the instrumentation of the plane. Exactly. And then you've got other data about the past. We are so excited about data. So if you think about weather, there's all those historical data. We've had observations at airports since we've had airplanes to do that. But you're seeing this fill in at the Weather Company. We have a network of private weather stations. These are weather enthusiasts like yourself that have purchased a weather station, have it in their backyard. Over 200,000, and most of our growth is global. So you're starting to see it go into areas where we've had a lack of observations like India, parts of Africa. Super exciting to see that. And in other traditional sources like satellites, we have the great big, I like to call them the sentinel satellites that the governments launch, but now we have all these CubeSats coming into play that are bringing really interesting environmental information. It's really a pretext to the trend that's happening because what's going on is that you're using crowdsource-like gamification of weather stations. Getting smaller and faster and cheaper with devices. And you're adding that into your existing system. That's right, and I love your tagline of extracting the signal from the noise. That's exactly what we're doing. If you have a lot of data like that, it's not going to be the highest quality data, but there's a signal in it, and that's what we extract. And you know what to look for too, because unlike they have one stream, you have other data that could tell you about that data. Exactly. So Mary, how does it work? You got this distributed device network that's collecting all this weather data. You got models. How much of the data actually goes back to the models? How much stays at the edge? Do you send the model to the data? How does it all work? Okay, so if you're going to run any model, you'd like to get the best set of highly quality control data to make it run. And we run a model at the weather company that we do, and we do it very custom for particular uses. So I'll give you an example over the northeast corridor of the US, that area between DC up to Boston that's so heavy, including New York City. We'll run a custom model for them with maximum information at a micro scale, really small scale. But what we really do to do our global solution is we take forecast models from all around the globe. We have about 160 of these. So we take the model output from the National Weather Service in this country, from the UK's Met Office, from the European Center, from the Bureau of Meteorology in Australia. We take all of those, and with a machine learning algorithm, we produce the best forecast. And we do it time and time again. You can see, we can be your best model any day of the week by machine learning on top of all of the models. So you begin to understand the biases of each of those? Absolutely, in real time. So if your model isn't tracking this storm particularly well, you're getting less weight in our, the algorithm understands that machine learning is adjusting on the fly. And the vast majority of the data comes back to the model, or is it sort of stay in place when you bring the model to the data? Well, for us, we bring model output with that, but model output is tremendously dense. You think of a model that's run every eight kilometers all around the globe. Think of time steps that are at least hourly, if not finer resolution. Think of it going out to 14 days in time. An incredible amount of data comes in and flows back and forth. I was mentioning earlier, we take in 100 terabytes of data a day at the weather company. We're ingesting that kind of data. We're talking physically, speed of light, coming into a central location, and then you apply the model there, okay? That's a lot of ingestion. That's a lot of ingestion. You get a lot of indigestion of the data. But that's interesting. So I want to ask you about that. So obviously, I want to, the ingestion's key, but one of the things that's coming up in the database world, this comes up in all of our shows we go to, you have database guys who are trained computer science guys who know schemas. Oh yeah, they can handle unstructured data. But what's going on now is that they ingest everything and think they're going to figure it out. But if you think about real time, weather's real time, time series data is not really good anymore after the time it's needed. So maybe think about staging that differently. So there's new practices emerging with time series specifically. What are you seeing there? Because you guys are dealing with this every day. You don't want to swallow the basketball, be that snake if you will, when you don't need it. So John, that's such a perfect question. You've really teed it up. So IBM bought us, I would say, for a couple of reasons. One is because weather impacts virtually every business. Number two is we have a great forecast. But number three, we're completely running on the cloud. And so we manage all of this data in the cloud coming and going. We have a data platform that we're now in the process of commercializing to do that because we've kind of solved that problem. Real time transactions. I was just talking to a customer earlier, running a nationwide set of golf courses has to have a really good forecast for the golf course. We're exposing our data stream to them and they said they've never had such an easy integration. We gave them the APIs they were up and running the next day. And what's there? Obviously, besides customer satisfaction, is there concern around like lightning obviously? Golfers don't want to get struck by lightning. What's the issue with golf courses? So a couple things. Safety is one issue, but it's about optimizing. And in that case, if you think about a golf course, you want to really minimize the number of chemicals you use but you can't afford to let pests come in. You can't have blighted areas in there. So they're running. We expose a couple of variables to them for temperature, precip and all. They know when certain pests will breed and they know when to do apply chemicals. So we're helping them.  I think you should think about weather forecasting is yesterday's metric used to be to make a great forecast. Today and tomorrow's metric is to make a great decision. So it's not good enough to have the right forecast. You have to be helping people make the right decision and that's what we do all the time. Well, Ginny Romney says in the commercial, a lot of the data's hidden. Bob Pichino has dark data. I'm not a big fan of dark data. It reminds me of the London Valley of the Web, but dark meaning, invisible. But that being said, you can only do forecast. That was your best outcome. Now you're saying, okay, that's table stakes. What's the decision that you make with the known data? So what we really want to do, going back to an airline example, so we want to tell them how much fuel to put on that plane because so hair is liable to be in a ground stop for a certain period of time and they're going to have a slower ingest rate. So we're jumping beyond what's the ceiling forecast, what's the visibility forecast, right to the impact and giving you the forecast. That's a great example of the plane one. I talked to the vice chairman of United Airlines on a panel I did with G.E. one time and he said literally they save over a billion dollars hard cash using IOT and data just on how to move patrolling them around for the fuels. Absolutely. You're talking about something specific where there's potentially life savings, but more importantly, it's real money. It's optimization. I mean, it's real cash that they save. It absolutely is and it touches areas you wouldn't think about. In the consumer part of our business, we can tell advertisers when to advertise and I'm not talking about snow shovels and snowstorms. I'm talking about when people buy more yogurt because they buy it under certain meteorological conditions. Very nuanced. Very nuanced and we have all of those insights. That's the signal from the noise. So you say that it's all about ultimately making the good decision, the right decision and it's not just about having accurate forecasts but that is, as John said, table stakes. And people like to joke about the weather. During that whole deflate gate thing, Bill Belichick was there. The weather forecast is terrible. They pile on. What is your data show in terms of the accuracy of a forecast over the last, whatever, in years, pick yours. So our overall forecast accuracy, if you pick a measure like temperature and getting that right within a degree, we're in the mid-80s, 80% to do that now. But what we really want to look- But the weather stage is now coming online. You're going to probably get more precise. Yes, we're getting more precise in that. I can imagine. But let me tell you one of the things we're really focused on because this is what's so important to so many of our users. It's not really what that temperature forecast is but when I'm going back to the gentleman I was talking about with the golf courses, he really wants to know when he's going to get a quarter of an inch or more of precipitation because that quarter of an inch makes a difference to him in terms of what he has to do for irrigation. So he doesn't really care whether it's going to rain or not. He cares how much it's going to rain and he cares exactly about the amount of rain. And that's important. And how are you doing there? Well, I can't cite a visibility thing for that. But my point is- That's a new challenge that you're taking. Yeah, it's a new challenge. And what we will do- No, that's the impact of the customer. That's an example of the impact of the customer benefit. Well, I want to even take a level like that. We can make him a forecast for that quarter of an inch but we'll give him a confidence probability with it. And then he can weight that with his other factors. And then learn over time, improve his or her model. Exactly. Okay, so what I just learned is it's not, like you say, it's not just about temperature and is it going to rain? What should I wear? You're being presented with new business challenges that are weather related that may not even show up on our little weather app. Absolutely. And I think every business needs to think about both how they can capitalize on weather and how they can minimize the costs of weather. Because I think there's both sides of the coin there. Mayor, interesting background that you have. First of all, this is a great conversation. We can geek out on weather. But I want to ask a personal question. How did you get into this? Because you have a computer science degree- Right. From Maryland, I noticed that on your profile. And you also worked for the US Secretary? Well, I actually spent 20 years in the National Weather Service. And I have about 30 credits in meteorology as well. I have a, so I'm old, right? We can see my gray hair. So in the late 70s, it was all about these great big computers and weather forecasting. So there was this nice marriage between mathematics, computer science, and meteorology. In fact, in Maryland, it was all in the same hallway. But how I really got into that was as a child and being really being impressed with storms, we got caught in, including one tropical storm with no warning. You got caught in. Right. Really? Our elementary school was closed down at 11 o'clock and we were released with the winds blowing and my mother would tell you the nightmare story of trying to hold on to everybody. Because we take it for granted now. And that impacted you. There was no satellites, no weather satellites, watching things. Well, the North East is on the East Coast is one historic storm that wiped out everyone. So you got into that. We'd love to have you on the more on theCUBE. We just had Grace Hopper last week talking about women in computing and going back to the old, to how math and the contribution in the industry across the board. But what's interesting is the global warming question. I want to ask you about global warming because that's something that didn't come up on the debates I was kind of upset about, that last debate. Global warming, how is this going to evolve in your mind? Obviously, how real is it?  It's pretty staggering. It seems to be very real. Yeah. So it is real. There's just no question about that. And we have tried to shift the language to climate change because not everybody gets warm, you know? Climate change, yeah. But things are changing. I think we're going to see these impacts and I think we're going to see a lot of them play out in extreme events. So we know already, if you go back and look at Hurricane Sandy, Superstorm Sandy that came into the Northeast, we can tell, you know, the science has been done to talk about how much more extensive the damage was because the sea level has changed dramatically, you know, or noticeably I should say, not dramatically over this past century. Well, dramatically given the storm, given the flood in Lower Manhattan. So I think that any business that's assuming the last 10 years of weather looks like the next 10 years is making a mistake. You're going to see more extreme events. It's, you know, it's common. It didn't used to be common to see three inches of rain, what we saw in Louisiana, you know, a couple of weeks ago, I guess it's been a month or six weeks ago now. Very common. So people that are, businesses and communities that are impacted by this are asking the question because the question really is, how often am I going to see this quote 50 year event? And the answer is you're seeing it a lot more often. A lot more sooner and more frequency of those. Is there anything in the data that you're learning now with cloud computing and some of the, you mentioned high. So let me say, make one point that I wanted to get in here. Okay. That's the question about how cognitive and cloud computing and all are going to help weather forecasting get smarter. And one of the things that we're excited about in IBM is the opportunity to have Watson look at the reams, go back and look at our past reams of data and help us understand some patterns. So for example, we know today that a warming pattern in the Pacific, an unusual warming pattern in the Pacific results in a drought in the middle of the country. What other patterns haven't we noticed and could Watson find them for us? So go dig through the archives. Go dig through those archives and find out what the scientists haven't seen. How big is the archive? Well, it's big.  How many years? There's also text paper. Has it been digitized? I mean. Oh yes, all of that's been digitized. The federal agencies in this case know I worked for a long time. The climatic data center there has all of those archives available. And you know, you have really good data going back to post World War II. And I think it would be, you know, we're anxious to put Watson loose on that. Well, they can throw some compute at that too. So you can theoretically sequence genomes in five minutes now. So potentially have Watson just grow. Yeah, and help us kind of figure out too. Maybe there's some things we can do. I'm confident there is in the forecast for the week three and week four and think about how important that would be for supply chains. And there's a predictive element here as well. Absolutely.  So that's another factor that has to be put together. My final question, Mary, for you is what's the coolest thing that you've seen that you've seen in the past couple of years? One, that you've seen observed in technology and weather. And two, the coolest thing that you've done. So I think the coolest thing I've observed, I think that some of the observing that's done from space is awesome. I'll put in a plug then. NOAA is about to launch a new geostationary satellite in two weeks and it's going to be a game changer in terms of the ability to look at Earth and it, you know. So I think that that technology of space base observing which lets you do the whole Earth is just awesome. The coolest thing I think I have done is stood on the Greenland ice sheet. Both the coolest thing and the most sobering thing is to stand on the Greenland ice sheet and listen to it cav off. So you talk about climate change, you could stand there and watch it. And within your line of sight, you could see where that ice sheet was in 1970 and how quickly it's eroding. That's moving, great. Mary, thanks so much. Great conversation. We could have gone 45 minutes talking about weather and all the coolness of the tech behind it, the intersection of technology and just impact the life. Thank you so much. Thanks, it was a lot of fun. Very glad to have you as the Senior Vice President of Science and Forecast Operations that the weather company, part of an IBM business, will be back with more weather and signal here in the Cube after this short break. I'm John Furrier with Dave Vellante. We'll be right back. Thank you.


These are the ROKID MAX AR glasses and they come with the ROKID station as part of the ROKID AR joy pack. This is all retailed at 499 dollars and they always have some offers and discounts running so make sure to hit the link in this description to find out more information about the specs and the latest pricing but this is a very valuable cinematic pocket android tv experience for you to have around the home 1080p 120 hertz refresh rate and just a full android tv experience that i'm seeing right now for all of your favorite streaming content apps directly through these lenses this is really great and i highly recommend anyone looking for ar cinematic glasses this is going to be great for you it's very versatile it's very portable it's very lightweight and definitely check out my youtube channel for the full review on seeing why this is my favorite ar cinematic android tv glasses on the market right now follow and subscribe for more gadget reviews like this


Every week I send an email to my mastermind clients. They send me one or two questions. Usually it's a science-based, kind of like tutoring question. And we'll talk about anatomy or physiology or whatever. I got a guy who's going through a physiology course. I've got a guy who's reading an anatomy book. All sorts of stuff. This week I had a very unique question. More of a business, career, mindset, life, existentialism kind of question. And I want to read you his email. I'm currently still looking for a job and I've been reading a lot about the process. I'd like to pick your brain on it because you have a lot of teaching coaching experience. I know follow your passion might not be the best advice, but my passion is to become a great physical preparation coach. However, I coach for free currently to get coaching experience and I'm not sure if joining a big box gym would be a good gateway to becoming a better coach or possibly for that matter if they focus on my formal education only. Therefore, my idea is to try out a job and sustainability in the meantime to pay the bills and see if I might enjoy it while I can build some tangible skills, which I do not have my choice for a very broad study. So he is from a country that isn't an English-speaking country. Some of this is a little backwards. Anthropology sociology is his undergrad and he has a master's in industrial ecology. He left three prompts for me. Do you have any remarks about this plan? I read slash heard about the idea of finding a job that meets your natural talents. I'm not sure what to think of it. Do you think you can and should identify these? And if so, do you have a tip on how? And then third, have you seen people trying to become a better coach or something else while it might not be a good fit? What skill set does a good coach need in your opinion? So there's a lot going on here. I have a friend who has a master's degree in economics and he's a fantastic coach. He's not even this ridiculously outgoing, extroverted kind of guy. He is great at connecting with people. He's great at giving advice. He's very intelligent and he understands his science so he can give great advice. And he understands the ultimate limitation is behavior. How will somebody be able to enact the advice that he gives them? So he's identified rate limiting steps and he knows how to push even semi-accomplished people further on. So there's a lot that goes into that. Let's break down his questions. So any remarks about his plan? The idea of making money and living and eating is a strong one. So if you are well trained and you can find a job in the field that you are comfortable in or trained in or familiar with, then go ahead and do it. I know for a fact that this guy is kind of having some trouble doing that. So maybe keep the feelers open. If you find something that pays really well, then it's probably something that you might want to take as long as it's not eating up too much of your time because we have to keep in mind what is the ultimate goal. And if the ultimate goal is to become a physical preparation coach, then you've got to prioritize that. That has to be there. It's really hard to see people go through jobs working for the weekend basically. This phrase is poisonous.  I don't think that my mastermind client here will hate doing that stuff. And as he said, he may actually like it, but I don't want him to force himself into something that's ultimately just going to bring him down. Those are just some kind of thoughts about his general plan. Now his second prompt here. Read and heard about the idea of finding a job that meets your natural talents. Can you identify these? Should you identify these? How do you do it? My favorite book on this topic is called So Good They Can't Ignore You. It's after a Steve Martin quote and the book is from Cal Newport. I read this thing and I thought to myself, man, I should have read this book five years ago. It would have saved me so much time. The idea is that follow your passion is not the greatest advice. And I've received that advice and I've received that advice from millions of smart people. Millions is an exaggeration, but it's nice and it's frequently given by people because that advice has a very simple actionable step. If you know where you're going to go, then take steps to go there. The issue is that, I mean, for me, exactly, I have no idea where I'm going to go. So it's hard for me to make plans more than 24 hours in advance, more than six hours in advance. So let's be honest, I can't even make my day plans the night before I got to do it in the morning. The book talks about acquiring career capital. Basically, get more skilled at the things you do and then at some point those skills are going to fall into place. They're going to make sense. As you have more tools in your toolbox, you can better apply those to problem solve. Another really good book about problems is the subtle art of not giving a F word. And it's by Mark Manson. If you haven't heard of it, I'd be shocked because it's like the number one Amazon bestseller right now. It's great though. And he talks about problems and he talks about choosing a path. He talks about what stresses people out, especially in this day and age where information is everywhere. Millennials get a bad rap. They're not all just misguided souls. They're looking for a place to go. And they're trying to find their purpose and what they're supposed to do, but they have no idea how to do that. And the world, the social media, makes it seem like they should already know those things. And it's okay not to. You just need to go out and you acquire career capital like Cal Newport says. And eventually things will fall into place. Third prompt here. Have you seen people trying to become a coach or something else when it might not be a good fit? What skill set does a good coach need in your opinion? What skill set does a good coach need? So I've seen a ton of coaches who leave a lot to be desired from my perspective, but from their client's perspective. Clients love them. You'll hear some of my PT friends will tell you stories about someone coming to them for physical therapy. And the person will say, you could ask them, what did you do for your last physical therapy session? Who'd you see? Sarah. Oh, Sarah's great. She is just fantastic. I love her. Yeah, I wasn't any better after though. And they have no associates. So they identify this person as someone they like, someone they want to be associated with, someone they want to be around, someone they want to talk to, someone whose opinion they respect, even if it is proven that the opinion holds no value. So number, absolute number one thing for coaches is you've got to be good at talking to people, making connections, caring about people. You know, for me, I'm not a very extroverted naturally kind of person. I can turn that on when I feel I need to. But most of the time it's less stressful for me to stay introverted, to listen a little bit more, talk sometimes. But ultimately, that's all people are looking for. They need someone to listen to them. They need someone to guide them. So make sure you can you can do that. For science perspectives, I just think whatever general basic science you find yourself most motivated to study, because some people find it really hard to study basic sciences, whatever you've got, whatever kind of drive you can find, whether it be anatomy or physiology, you just latch onto that as much as you can, and you learn as much about it as possible. The more basic science you understand, the more creative you can be down the road. You can make up exercises, you can tweak programs slightly. You know, when you start coaching, you learn these programming rules. And then when you understand your physiology or your anatomy or whatever, you can start to break those rules. And you can start to form programs around your methodology. You start to develop your own system. You know, you might take a weekend course and take on their assessment process. And that's a great place to start. And it's a great way to test to see if this assessment process is going to fit into what I do. But ultimately, you have to keep asking yourself, is this going to fit into what I do? Does this fit into what I do?  So be able to make connections, learn whatever basic science you need. And then the third main point is you've got to get out there. You've got to practice. You've got to practice talking to people. One common piece of advice for people who think they want to coach is to get a service industry job. If you are a server, waiter, waitress, or you work in some other restaurant or you sell clothes, all of those give you experience talking to people and managing people, especially people who are angry and expect a lot out of you. Because that is going to happen. People are going to yell. People are going to get upset because that's the nature of humans. You need to learn how to deal with that stuff. Okay, so we've got learn how to be a cool person, a genuinely nice person, someone who can connect with people. Learn some basic science and start doing it. Get practice. Those are my basic, overlying 10 or so minutes of thoughts. If you have any other thoughts to add, leave them in the comments below and don't forget to smash that like button. Do you start something and then just quit it almost immediately? Do you have trouble with accountability? Are you ready to start your personal career as a physical preparation coach?  If you'd like to sign up to be one of my mastermind clients and exchange weekly emails, there's a link below. Click it. Huge dividends will pay off.


What if I told you there was a professional CRT company that used to sell CRTs in the early 1990s and they were using a shadow mass technology and all of a sudden they saw the Sony PVM and they were so blown away with it that they decided to completely end all manufacturing and ties to the shadow mass tube and jump over to the Trinitron and basically let Sony manufacture everything from this point on. Now the company I'm referring to is Olympus. They originally made a shadow mass tube with the help of Panasonic. Here's a look at one of the 13 inch sized OEV 141s and again this is pretty much a shadow mass tube made by Panasonic. If you were to look inside all the hardware in there for the most part is from Panasonic. It's an RGB monitor but all the sudden Olympus switched over and started using Sony's products and that's what we found here today. So what we're looking at is a Olympus 143 13 inch video CRT monitor. Now I bought this and it's supposed to be shipped from New York City so I've purchased this one. We're going to go up now and open up the box because it's been delivered and we're going to see how it came out. All right here's this box we're about to open. I have covered up my shipping address label but I wanted to show you the box so you could see that it came in fairly good condition but maybe the more surprising thing is it looks to have come from Japan and that's just incredible to me that this could come from Japan and even be profitable at the price I bought it at but let's open this up and see what we've got inside and how they packed it. Time to open it up. So we've got a proper box here. Check this out. It is a double wall corrugated box, new box. So that's perfect. That's the exact box you want to ship a CRT in. You don't want to go with a single wall box that won't work. Okay yes cool. Wow check that out. This foam is fitted to go right over top of the PVM and there's the PVM right there so let's get it out. Hopefully nothing happened to it. There's one more piece of foam down there in the bottom and it's that expandable foam. Let's set our PVM down and see what we got. It does have a little bit of bubble wrap. Let's see what the CRT looks like. All right here's our CRT. We're going to do this all together. I'm going to remove this bubble wrap and hopefully this beautiful CRT is still okay. What the heck? Look at this. This is a Japanese version in the world. Oh my goodness. OEV143 that's all correct. Tube feels solid. Okay that's good. Everything looks normal over here. Let's see what we've actually got. This one was distributed in Japan. I mean it's the same kind of model that would have been in the US. Look at this we get a rating from 100 to 240 volts 50 to 60 Hertz for power input. Here's our input board. Oh that looks pretty crusty. Just looking at the input board here. Check this out. These are little characters right from the alphabet in Japan so we think that says red, green, blue, sink. Very interesting. Sometimes these pull tabs will break. That one seems solid. This one too and take a look at the bottom. If we look down there I don't really see any damage down there. That's all good. All right we've introduced power and this is US power 120 volts alternating current. I haven't tried the menus yet. I'd like to see if that is just going to be a normal procedure. Yes so I thought there might be a chance that this was configured to be in another language but that's actually not the case. It's in English. We're in under scan mode so that's regular scan mode. The buttons do appear to be lighting up and working down here.  There you'll see that. All right let's have a little fun today and start by testing with the laser disc player chapter one. Oh my goodness. Look at this. There you go. I mean it looks pretty good so far. Maybe we should check out something a little bit higher resolution. Let's take a closer look at our 240p test suite now and we're going to roll through some test patterns. We have moved over to RGB and the first thing I like to do is run through the solid color screens and see if we notice any discoloration. And here we have again discoloration. Check it out. You can see it very well here. This magenta corner that's a magnetism issue. If you look up at this corner, same thing. Magnetism issue is causing a discoloration in those two corners. There's a degausser built into this monitor and we can try to use that to clear this up but it's just not strong enough to get rid of that. So that's one issue that we're going to have to try to attempt to repair with our magical degausing wand. We are going to try to correct this magnetism imperfection with our degausing wand here. I've got a couple of cameras set up so that this will hopefully work right and look good. We're going to disrupt the colors there a little bit. We're going to go in a larger circle over here. We're going to kind of get close in there like that and as we back away we're going to expand the size of the circle. Now our issues up here have gone away. You can tell we still have a little bit of magnetism here. Now that makes me think that this is more of a polarity issue where I'm getting magnetism from this room. So let's spin this monitor around and see if that makes any difference. So I just rotated the CRT 45 degrees and check it out. The screen's completely cleared up. When the screen faces this direction, check it out. We actually do get some kind of magnetic interference and that could be somehow amplified. Maybe it's amplifying the magnet from under the speaker for all I know but we're definitely getting some interference when it's facing this direction. Now in the monoscope pattern we don't actually see much issue. Now it's not great. The centering isn't perfect. We may even have a slight bit of crookedness along the sides that hopefully can be corrected with just a simple hour spent running through the geometry menu settings and making adjustments to that. The convergence is actually really nice in all areas of the screen meaning I don't see a lot of separation. Normally you might see that at the edges in the corners, a separation of your colors being your red, green, and blue. Now this is a sharpness pattern and it does kind of help you see the sharpness of a screen like this. This particular monitor has a 600 line resolution Trinitron tube in it so the resolution and the sharpness is quite nice on this tube and you can definitely see visible scan lines the closer you get to the screen. I decided to look inside our PVM and honestly this thing looks exactly like the United States counterpart to this. All right this is an Olympus 203 so it's the 20-inch version of the Olympus Sony PVM and this one has been serviced already but it still has an issue and I'm going to show you what that is. First we're going to turn this on. Well I've just powered it up and I hope you can hear a sound where it just sounds like a loud ringing and that's actually coming from this little part here that I tried to cover in caulk before to eliminate the vibration but we need to go ahead and just replace this part. Listen though so you shouldn't do what I just did where I just touched that with my hands but that's one of the ways to find out if these parts are bad if you want to find out which one exactly is vibrating because any of these coils can end up vibrating and causing that noise distortion. Now I went in and replaced that coil this was the old one that was making the noise and it seemed to function properly it was just really noisy so that one's been replaced we're going to turn this on and hopefully we're going to just hear the normal high pitch wine of all the other transformers like the flyback transformer and we won't hear that singing from that coil and you see do hear some of that because you're still going to get that noise from this flyback over here so you're going to get some of that buzz and high frequency noise if you can hear it not everyone can. Alright so first off I checked the tracking on this package and it definitely did come from Japan and honestly I wasn't expecting this to be my first import of a pvm but officially this is my first import of a pvm and for the cost that I paid for this on ebay I can't believe that it was even affordable to ship this overseas to me from Japan but who knows what's going on there so it's really cool to see the Japanese model of these and how it's pretty much the same as the American model and it is always fun to show off the degausing wand and try to get that magnetism cleared up on purity problems I just love using that thing so I was glad to be able to pull that out. Kind of the last thing I want to say here is that it doesn't really matter which one of these monitors you run into the older ones that are made by Panasonic are still really nice but to me the Sony ones are better and they definitely stepped up the technology game this was the era when they were able to add the first menus like the on-screen menus and the service menus used to be able to get really good deals on these because nobody really knew about them or knew that they were the same as the sonys but I think pretty much the time has passed where everybody knows that these are pretty much just sonys with a different label on the front down there and on the back so anyway I hope you enjoyed this video I'm going to finish out today by showing you some really cool footage of an Olympus OEV202 and 203 so I hope you enjoy that and I'll see you next time with some more retro content