 Good evening. Good morning. Let's see if I can get this thing to work. That will be my first test. Okay. So, some of you are wondering what G Executive is doing in a conference like this. It's almost like a crop duster coming to an organic grower conference. But let me give you, take the next 15 minutes and try to explain what we're building. Maybe by the end of the talk I'll be able to convince you that we are working on the same thing that you're working on. Internet of Things. IoT. Very important thing. You can see it everywhere. Everybody's talking about the numbers of devices that are showing up, the storage that is going on. You have four or five very important companies working on that. Google, Amazon, Apple, maybe Microsoft sometimes when they feel like it. Overall, these are very important companies and they have about 19%, not only they, they have about 8%, but overall the software industry is all about 19% of the S&P 500 total market value. Very important. Egg-cookers that connect to each other, communicating toothbrushes, belt buckles that light up, all kinds of interesting things. This is not what we're talking about. We're talking about something much more important. And you may not feel it, but at the end of the day when you open the faucet, you're using G equipment at the other end of the line. When you turn on the lights, there is a turbine at the other end that actually produces this power, this wattage that you're using. If you're taking on a flight, which some of you are going to do after I'm done and the next speaker is going to be done, you will be flying 70% chance a G aviation product. So overall we're talking about big, dirty, noisy machines that are very important for our life in general, our well-being specifically. Now the interesting thing about that is all those things have sensors and they talk day and night, 24-7, and somebody needs to analyze them and make sure that we understand exactly what's going on with them. If you look at the numbers of the sensors on those machines, we are approaching to the point where there are 20 billions of them and they create about 80 exabytes of data. Now this is a lot of information and the reason we want to collect it is not to break any privacy rules or do anything of that sort, but really to understand how we can optimize those assets potential to generate more revenues, how to make sure the production and the livelihood of those companies that actually are working with those assets much smoother and how do we make sure that we can extend those assets and allow them to generate revenues in the years to come. Those things are actually going on for a very long time. Some of those assets were put in place 30-35 years ago, naturally they didn't have all the controllers and the computer environment that we're looking at today, but the goals of GE and the companies that are working with the same ecosystem is to make sure that we really build this thing we call the industrial internet. You have the consumer internet, you have the enterprise internet, Salesforce, Splunk and so on, and you have the industrial internet and the industrial internet actually is going after that part of the GDP that is a little bit the underserved community, right? It's not something that every day you wake up and with thinking about how a turbine talks to another piece of equipment on that internet, but we would assert that this is really tomorrow's growth opportunity. There is a big difference between the two. At one level they don't look that different, but actually if you think about that, there's a huge difference in several key dimensions and that's why we're here today. First, the average longevity of those devices and no offense to the toothbrushes and the rice cookers is that usually people use them for about six months. It can be a nice woreables that you bought. It can be a nice gadget. These things actually are working for about six months on the average. Our assets need to work day in, day out for about 30, 35 years and when you think about that actually a lot of people have to make a lot of the operators have to make those decisions about whether they take the drill into maintenance or forget about maintenance and keep drilling oil because the price of oil is jumped by five dollars. Those decisions are really deep economical decisions that people have to make as they go and work those assets. If you think about the other part, connectivity, the biggest problem that we can have is that coal will be dropped. There definitely are certain loss of productivity when people can talk to each other, but when you have a block preventer that you have to overhaul or upgrade in the middle of a subset drilling operations and you know that if that block preventer is not going to work the way it should then God forbid another oil spill is going to be upon us. This is where connectivity is becoming very important and so not only we have to make sure that we will be able to get this connectivity all the time but we also have to make provisions for when the connectivity is intermittent or really to some extent unavailable. If you're in the middle of the sea, usually unlikely that you're going to have a cell operating in the vicinity, you have to rely on satellite communication, you have to optimize the cost of that, you have to make sure that under any circumstances the industrial assets will be able to keep working. Big difference. Data management. Let's talk about how many exabytes we need. If you think about that, there are about I think 10 million or 10, 10 million. Yeah, something along the lines of every second of tweets that are happening in the network. There's about 80 gigabytes a day and this is basically a very respectable number. If you look at Facebook, you're talking about hundreds of petabytes a year. Now let's look at what happens when you deal with G aviation and this is only G aviation. 500 gigabytes for every flight. It's all the data about the engine, the airframe, the vibration, the cabin temperature. It's important for mechanical engineers that build the airframe. It's important for G aviation folks that build the engines and it's important for the marketing and the performance scientists in the airlines that would like to understand whether the cabin was too warm or too cold. There are about 100,000 flights every day that are happening in the United States. If we only took the part that we are dealing with, which is GE is dealing with, which is about 70 percent market share, we're talking about 70 percent of 100,000 flights times half a terabyte. I think in my math it's about 50 petabytes a day. So we're talking about unprecedented amount of information and for the first time in history people actually know what to do with all this data. Because if you go and analyze that you're going to figure out how to build better engine, faster engine, how to make sure that the blades, the turbine blades actually can last much longer. So you have an end-to-end picture of everything that we're building. Security. Everybody wants the devices to be secure but would you pay a hundred dollars more for secure phones? Maybe you're in the intelligence community or a very, a business that is exposed to industrial espionage, that may be the case. Normal consumers don't pay for that. Our industry will pay a lot to make sure that their devices don't get hacked. I mean I'm not sure, I don't think I'm telling you any secret, but you can actually hack into pretty much everything unless there is enough security in that. And if somebody goes and farts around with a turbine or an MRI machine or a CT scanner, this is actually have some very important and very unfortunate consequences. Most of the things we're building are considered national critical infrastructure. They have to work all the time. We want to make sure that the end of the day the industrial internet is not only high performance but also high security. And we cannot tolerate breaches either by nation states or just by bad people that would like to play around with some of those assets that we have at our disposal. Zuckerberg said several years ago that privacy is no longer the social norm. And to some extent, while everybody was upset about that, it is true. We are much less private today than we're 20 years ago. It's okay to put things on Facebook. It's okay to tweet whatever the heck you think about. But at the end of the day, when it comes down to a different set of devices, again, the industrial devices, the medical equipment, this is where the privacy is not just a matter of sentiment of somebody or a doctor or a patient. It's a regulated and required property of the overall system. You have to be YPA compliant. You have to be ITER compliant. You have to have ISO 27000. You have all these standards that some of us may or may not agree with the fact that there are actually have some sort of a burden on our system. But it is what it is. The politicians, the people, the governments have all decided that this is how the set of equipment that we are building has to behave. And we have to make sure the industrial Internet, while at the risk of being a little bit more bureaucratic, is something that enables us to really serve and comply with those standards. So with that in mind, we had to build something that will allow us to really take all this data, make sure it's secure enough, it's compliant with the right standards, ingest it, store it, clean it, and make sure that at a certain point we can take all of that, analyze that, and come back to the operator of those assets with insights. What is that they need to do that will make the operation of those things better, more profitable, and prevent the bane of the industry that we're dealing with, which is called unplanned downtime. Unplanned downtime is when you sit on an airplane and the captain comes in and says, well, this plane is not going to fly today. Everybody gets out, everybody goes find a different plane. This is something that was not maintained, it was not planned. If it were planned, then you wouldn't be sitting in this plane where it should sit in a hunger and get worked upon, right? So at the end of the day, this is what we're trying to optimize. We're trying to optimize the operation of assets. We're trying to give our operators some insights about what is that they need to do. So we had to focus on higher level of the stack and for that we need to find the right level, the right lower levels to make sure that we can do it at scale. GE is working in 170 countries around the world. We have to make sure it's reliable because our customers don't expect anything less than 24.7, 4.9, sometimes 5.9s, and we want to make sure that we can get something that will allow us to push the envelope as we build the industrial Internet for years to come. And this is what where we started working with Cloud Foundry. It was not actually a natural journey because for GE, a company that has been doing what it's doing for 130 years to go and change everything we know about software, getting the Cloud Foundry culture, working with Pivotal and other players in the ecosystem was not very simple. But what it did, it allowed us to actually get, move a lot of the people that were working on that level in the past and deploying them on a higher level of the stack so we can work on the analytics and we can work on the way to ingest and keep the data and make sure we can get into the right data at record time without sacrificing the reliability or the completeness of that. Some of the data that we have to keep, we keep because we like it and because we think it's going to be good for us to understand what the assets are doing. Some of the data we have to keep is because we were told to keep it, right? The government told us to keep it. These are regulated data that we cannot get rid of. So Cloud Foundry was paramount in our ability to lift the discussion and lift the focus of what we're doing to a completely different level that will get us closer to the industrial internet abstraction. What is an asset? What is a locomotive, really? When you think about it, a locomotive is a big thing running on railways, but at the same time, it's 150,000 parts traveling together. And sometimes you have to look at the locomotive as the big hunk of iron. Sometimes you have to look at the part and figure out where's the right depot, where's the spare parts, and where are the other locomotives that that part is installed in? All those things are things that we have to think about it and solve it ourselves because, frankly, there's nobody in the ecosystem that will do it. Other things, we rely on Cloud Foundry. So a case at point is a wind turbine. If you look at a wind turbine, it's a magnificent thing, by the way. If you get all the way up, it's awesome. But if you look at that, if you think about what we're doing, wind turbines, it's like a wolf. It's never, basically, it never hunts alone, never deployed alone. Usually, you have a wind farm. And the interesting about wind farm is that when you have all this wind that is coming to you, you can optimize the operation of a specific turbine, but by doing that, you're going to create wake turbulence that will compromise the operation of the turbine behind that. So what you want to do is you want to create the analytics that will allow you to optimize the performance of the whole farm. And so some of the execution will have to be in the predict system, some of the execution of the analytics systems have to be at the controller level, and some of the execution has to be at the specific turbine. All those things have to be dynamically balanced because sometimes the issue is local, sometimes the issue is regional, and sometimes the issue wind and everything else is much more sectorial in that respect. Now, the data that you get is about happening. You collect data that if you look at that, at the time series data, it's pretty much every fraction of a microsecond. And so there's a lot of data to ingest. We have to put it somewhere. We have to now analyze that that what Cloud Fundry does for us or that what Cloud Fundry enables us to do as we go and solve the predicts problems. So to that extent, we had to do two things. We were excited to do two things. One is to build the incubator for the industrial protocols. If you think about what we're doing, we have a lot of data that we need to generate. Those pieces of data cannot just flow on an HTTP channel, which is what we got from Cloud Fundry. But in the best period of open community, we have built, we are working on the FTP level of protocol routing in such a way that we'll be able to take those industrial protocols and move them into the cloud. And we are contributing the result of that project back to the open source community. And the other thing, it looks like everybody, every real person need a dojo. So we have a dojo too. And this dojo is all about the industrial Internet. Industrial Internet is too big for GE, even as a 350,000 people company to take on itself. And so this is something that we're opening up on the other side of the bay in our local headquarters in San Ramon. And what we would like to see and what we start to see now that other than Pivotal and GE and some other distinguished members, we would like to see pretty much representative of the whole community coming and work with us on really pushing the envelope on what we call the industrial Internet. At the end of the day, this is just the beginning. So now that I explained why GE is here, I'm looking forward to being here again next year or any time in between and actually working with you folks and anybody else that wants to come and join us on really pushing forward this industrial Internet. I think it's going to be great. Thank you very much.