Research Computing and Computing for Research
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Wildermann, Sven
Tipp, Brad
In this Keynote Sven Wildermann and Bradley Tipp will be presenting Microsoft’s vision on AI. They will elaborate on which research milestones and tipping points have been reached - and which cognitive services are already available for developers today. And touching upon one of the key challenges for AI, Microsoft’s experts will showcase how “AI for All” is happening already, making artificial intelligence work for everyone.
 I have this weird job title, which says Director of Higher Education Research, and everyone suddenly thinks that I work for Microsoft Research and I'm not nearly bright enough to work for Microsoft Research. My role at Microsoft is to try to help the research community take advantage of our Cloud Services of a software stack, which is exactly what you guys are doing. One of the reasons that we are working very closely with RSE Communities globally is we're trying to do the same thing. I'll say right up at the front, yeah, I know I work for Microsoft, but I don't really care about that bit because my colleagues, AWS who are somewhere, one of them used to work with me at Microsoft, he's up the back. We're all in the same boat and trying to achieve the same things at the moment. So we have much more in common than we do against each other. There's three things I'm going to try and do in the next 45 minutes. One is tell you stuff that you already know, but I think maybe it'll be ammunition for some of you to help in the process of building the RSE community with people outside of this room. The second is talk about some of the things that we're doing with RSE Communities around the globe, and then Sven's going to do a bit of a deeper dive into our AI platform, and just cruise along to give you a bit of a view about other things that you can do. Hopefully, it'll be interesting to you. Last thing, I'm a Microsofty, so I have tons of slides. I've seen them all. They're really good, but I don't need to see them. So I'm happy to take questions. It's much more important to me that you get questions answered than I get through my slides. It's probably more important that Sven gets through his slides, but I really don't care. So if you've got a question at some point, stick your hand up, makes it way more interesting to me, because I'm on Seattle time still, and it's just that afternoon bit where I'm starting to fall asleep. So I'll go into autopilot on the slides if you're not careful. So it's your choice. You can ask interesting questions or I'll go into autopilot mode. There you go. I warned you, as they say. I'm lying on this slide. First slide, I'm lying already. Why am I lying? Because the we doesn't apply to us. The intersection that everyone's out of the moment is the move to Cloud computing. Whilst there's been a lot of movement, we're basically not even scratching the surface yet. When I say we are not even scratching the surface, actually I mean you and the institutions that you work with. We, i.e. Microsoft and in the case of Amazon, are we don't have a concept anymore of on-prem stuff. We don't have a set of Cloud data centers that we develop against, and then some local data centers that we use for something else. We just have this set over here. We closed our last kind of on-campus data center about three years ago. So we're not an intersection. We're way past that intersection. But you guys still are. A lot of your institutions are not even at that point yet. The amount of, I'll be provocative, the amount of new HPC on-prem things that I see coming out, tenders, unlike seriously, what year are we in? I still see people doing this and they're going to keep doing it for a while. When you get evolution, it doesn't mean that everyone evolves at the same rate. But actually, you've just got to look where the graphs are going. It's disappointing to me in a way that people don't plot the graphs ahead far enough. It's disappointing to me out of not you as individuals, but some of the institutions that I deal with, because there are really bright people there whose job is to plot graphs for everyone else in the world to say, this is what's happening. Yet there seems to be some intransigence about looking at their own stuff. So there is an intersection. We're not at it, we're past it, but we still need to get people through it and that's going to take a while. You guys all know what's going on. So this is the stuff that you know. Big data cloud processing, machine learning. I hate the brackets AI. Yeah. It's machine learning still to me. We'll get to AI, I'm sure at some point, but it's machine learning. But it's really when you can combine that together, it feels almost like AI. To the man in the street, it may as well be AI. People in this room know it's machine learning. That's changing the way the research is being done. Yeah. Again, this isn't new stuff. We've been going through this change for a long time. Fourth paradigm was written a long time ago. If you come a bit more up to speed, you've got people like Tony Hay who used to work for us saying, you really have to look at the rate of acceleration, and you then have to look at those graphs again to see how this is going to affect everybody. It's interesting to look back slightly and say, wow, weren't those numbers really small? 750 megs seemed like a lot a while ago. I was reading, Apple did their new launch yesterday, and you can put 1.5. I'm going to get this wrong because it sounds wrong. You put 1.5, I want to say terabits of RAM into the thing. I mean, I'm like, really? The numbers are just crazy. The LHC is still spitting out more data than anyone knows what to do with. It's a good job we throw two orders of magnitude away before you try and do anything with it. But really, it would be nice to look at those two orders of magnitude. Today, we're led on a path that says, go look at this tiny little spot in the spectrum because we think there's something there interesting. We have data for all this other bit, but we have to throw that away to look at this single spot. It would be nice to run some machine learning against the rest of it to say, wow, that's interesting. There's something over here that the theoreticians haven't actually discovered yet, thought about. That's what's not happening. Then when we get into the square kilometer array, things get really wild. In fact, they get so wild that people are saying, how are we really going to cope with this data? Big data centers is probably the answer. So it's always nice to show you pictures. Here's a small data center. You can tell the scale because that's a car at the bottom running along a road. This is actually two, you're looking at three generations of data center in here. Let me get it the right way around. The right-hand side, the left-hand side as you look at it, is one of our C-series data centers. Then you can see evolve into these modular blobs. Then at the top, you can see this backbone piece, which is the next series. So you can see as we build these things out, we're constantly evolving them. But actually, this is quite an old one. If we look at the top of this, the top bit of buildings is the bit in the previous picture. This is quite big. This is about two miles long and about three-quarters of a mile wide. This is one of dozens that are being built. The biggest challenge to us today, the biggest thing that slows us down, is how fast concrete will set. You still have to pour a concrete base to put something on. Trust me, people have been looking at doing, what could we do that's quicker than that? Then you fill these things. We'll get onto what we fill them with in a minute. But the point of putting this up is just to re-emphasize the scale thing. We all work in an industry that is using ever more amounts of data, ever more amounts of CPU, ever more amounts of bandwidth. So you're pretty close to the coal face. But actually, you still have to step back occasionally and just remind yourself, this is where we're going and this is an accelerating path. So if we're building these things today and sinking things off the Orkneys underwater like we did last year, what you have to look at is where we're going to be in three or four or five years time. UKRI doing a 10-year study, I'm like good luck if you can predict 10 years out at the moment. I mean, you're doing really well. Then we're not just doing this in one place, obviously, we have a few of these scattered around. So I think this slide normally needs updating when I do it, but I think I'm still accurate that there's 54 current regions. There aren't 54 current regions because how nice it is for us to put a region in Brazil so that Brazilian software developers can use local resources. No, the reason we have a region in Brazil is that European companies sell products into Brazil, and they want that connected thing, car, coffee maker, freezer, whatever it is. We used to laugh when we said that and you can buy all of those things today. They want that to work locally against a local data center, and they need it to work locally because you may have restrictions in that country that say, you can dial home but not with all the data, only with some of it. That's why we have data centers in China and India and South America, and South Africa, and go on and on and on. Yes, the ultimate extension of this is one at the bottom of your garden, because we're literally rolling these things. I think France was the last set that we went live on. I don't know, I lie. UAE was the last set that we went live on. This is happening more and more and more and more. Again, think about the scale and think about what that means to you, and think about what that means for the resources. The same with the network. We're not just using the networks that exist, we're buying, building, and laying the new networks that are out there, because you have to when you're moving data at this kind of scale. When you're failing over between regions, if necessary, because it's not just failing over between those data centers, or failing over between a twin set of data centers in one region and another one in the same US region, it's about failing an entire region over to another region if you need to do so. You have a tsunami in Japan, you fail everything over to the US. That's what you have to do, assuming your customers say that it's OK for you to fail over to the US. A lot of people wouldn't be happy about that. So what do we put in them? Everything that you would imagine, frankly, proper HPCs within Finneband, FPGA systems as well. Probably only 18 months ago, I used to put this slide up and talk about FPGA systems. Even an audience like yourselves would say, wow, that's pretty esoteric. There's not many people doing FPGA programming for the systems that they're working on. Even in the last 18 months, this is becoming more and more and more mainstream. The guys at CERN are using these, because it's the only way for them to get the throughput that they need to do some of the work that they're going to do. Equally, I've been working with a research group in the Netherlands who are doing neonatal work and they were doing genome processing. And they've really needed to get their processing times down into the couple of hours margin because you don't have that amount of time with neonatal patients. You just don't have hours and hours and hours or days to wait. You have to do it quickly. So even in the last 18 months, what was really esoteric 18 months ago is starting to be more mainstream. Well, common, let's put it that way. And then if you really want to go the whole hog, you can stick crazy systems, or I suppose I should say HP systems these days, in as well. And again, the interesting thing about this is most organizations in most parts of the world just can't afford to put that kind of infrastructure into their own organizations. But now they don't have to. One of the things that we're doing here, and again, not just us, my colleagues at AWS are doing exactly the same thing, is that you're democratizing the capability to do research. You're saying to someone in a research institute in South Africa, you can have access to all of the compute you could ever want without any upfront payment at all. And you can buy it by the minute if you'd like to do that. And you can buy the kind of scale systems that you used to dream about. And so you can attract researchers to your institution, not based on the capabilities of that institution, but based on what the rest of the institution can offer them, or great surfing if you're near Durban. So this is changing the nature of some of the institutions that you work with, and it's going to change the nature of how things get done. But it's a democratization process, and that's a good thing in my view. I'll stick this slide up. All these slides are available, so you're not meant to read them in brief, because everyone is rolling out these new systems literally weekly, which is, again, another point. It's like we and our friends at Amazon, and if they were here, our friends at Google, are investing in the hardware in a way that nobody else is. So it means that you're getting access to new systems, basically as fast as they're available, which means that you are not compelled to buy something and sweat the assets over a number of years. We have to do that, that's our problem, and we'll continue to do that, but it stops becoming your problem. And this is just the latest bunch that have rolled out. Now, okay, the hardware is great. What about the stuff that runs on it? Because, let's face it, I've been a Microsoft a long time, convicted monopolist, lived through many years of DOJ trials. Not a pleasant thing, frankly, as an employee. I like to think we've learned from it. Actually, I think we've really learned from it. And when I look at how we work now, it's kind of weird to me that the world's largest installed base of Windows systems is at AWS, and we run more Linux clusters than anybody else. And we make more contributions to Linux, not just Linux, actually, to open source repositories than any other, quote, proprietary software company out there. So it's not the Microsoft I grew up with, that's for sure. I like to think we've evolved past that, but I have to make the point, because I know most people still don't see that. And it's not just a one layer. We have to do it at all layers to be able to do this. We have to really make sure that everything that you want to do, you can just do. And that's a complete change from us. We have to make sure we trust it as well. So every certification that's out there, we've got to make sure that it's available, because a lot of the organizations that are doing the research with you, or funding the research, need us to meet these requirements. So you saw us roll out FedRAMP to every region in the US last week, because surprise, surprise, the US government would quite like us to be compliant with that. So we have to be open and we have to be compliant in order to be trusted. And that's stuff that you know, but you can use with others who don't know. So what about you guys? Why are we here and what are we doing? Well, the first thing is really, when we thought about this a few years ago, the classic Microsoft response would have been, we'll go and set up a Microsoft community. We'll do this Microsoft thing and then we'll invite people to join it. Yeah, and some of you probably would have joined. Thought, okay, there's probably free beer, so I'll go along. And it would have been okay. It wouldn't have been okay. It wouldn't have been great. And I persuaded a lot of people at Microsoft that that is not the way that we should do things. Actually, what we should do is look at the way that the UK had built its RSC community and then work with other organizations around the world to kind of go to you guys to turn up where you are on your terms, which is why we're here speaking at your conference, not a Microsoft thing and why we're working with the South Africans, the Nordics, the Dutch, the US guys to try and get the same approach going there. I have to tell you, it's harder. Yeah, it's a lot harder and some of my senior managers kind of look at me and think I'm a bit mad, but I think it's the right way to do things. The other thing that it does is it means you don't have to worry about us losing focus because like most software companies, the focus shifts dramatically sometimes. If we had set something up, the danger is at some point somebody decides to defund it and then you guys go, what about us? Yeah, you don't have to worry. It's your thing. You never have to worry about that with us. You may decide you want a different sponsor next year. Great. That's a pretty minor thing to have to worry about. What you don't have to worry about is us suddenly disappearing in your organization going up in flames. We want to come to where you are and we're doing that by investing in the community in a lot of different ways. We're putting a role that we call a research engagement manager. We've got one in the UK at Imperial, we've got one in Australia, in Melbourne, one in Hong Kong today. We've got a different setup in the US because we're working with Indiana University to roll out a program on our behalf there, but these people are not Microsoft people. They're actually researchers or their research associates or their RSEs. Again, what we're trying to do is to build bridges into the community and to say, tell us what we need to do. Tell us how we need to work. Tell us what you need for you to be successful because fundamentally, if you're successful, we will be successful. It's not hard. It's sometimes hard for me to get my management to understand that, but it's not hard. It's just the way we should do things. Next year, you're going to see us do a lot more in terms of, I use this terrible training word here, training for the research community. Actually, what that really means is something that we did two weeks ago at Imperial. In fact, we did a mixer at Imperial and our reactor centre in London where we got a group of interested people together, some of whom are RSEs, and said, tell us the stuff we need to fix. Tell us the problems you run into. We ran through a bunch of scenarios with them that they had brought to us, not that we had started with. Then we're looking at, okay, what were the roadblocks that people here are? There's this issue in AAD provisioning that doesn't set things up correctly. If you try and then use it as an RSE, if you're using AAD, you don't get to provision the services that you want without making some basic changes. We need to change that. Great. Our engineering people love that. It's like, this is what you need to fix to allow people to use your service more. Then we're publishing all that information out onto GitHub and out into our technical documentation on docs.com so that it's there for anyone to take advantage of. Again, it's not our stuff. It's your stuff. We're just providing a place for someone to be able to host it and people to be able to go and find it and link to it and work with it in the future. Hopefully, that's what we call training. Because if I don't use that term internally, people really don't get. Most of the people I work with have no concept of what you guys do anyway. Just let me use the training word on here. Don't take it to heart too much. The other thing that we're doing is we're trying to integrate all of our technical documentation and all of the GitHub repositories that we and you create together inside the main Azure portal. You'll see this roll out at the end of this year as well. This means it's really easy to go to one place, start a project, understand where you need to go in order to get the documentation to help you with that project. If you're working on something new, how easy it is to be able to push back into the community so that everyone else can use whatever it is that you're working on. If we can make that cycle easier, then hopefully we'll help you guys in your work in trying to help the researchers get their work done. Our ultimate goal is just to try to help researchers get their research done faster, get their publications published quicker, and make your lives a bit easier in doing that. And to try and help you guys stay up to speed. Because as people said earlier, this stuff is advancing faster and faster and faster. The biggest problem is actually staying up to date, making sure that you're taking or able to take advantage of the latest developments and the latest piece of things. Software is really eating the world. Every organization really is a software company these days, which is kind of nice. Certainly should be good if you're in the software business. Every organization is depending on people like you more and more and more. It's just that not all of them realize that yet. I like to think that we do and that we're going to try and invest in this community and in building these communities more and more and more in the coming years. But at least you understand what we're doing and why we're doing and the acceleration that's going on in that. I'm going to hand over to my colleague Sven who's going to talk you through our AI platform just before I do that. And just to check you're awake. Any questions at this point? Yes. Yeah, fundamentally. The question was what's the business case? How do I get funded? Yeah, Azure is what the company is betting on. I mean, you're still here. People talk about windows and office and stuff like that. But frankly, it's Azure services or bust. It's just infrastructure like your services working on the infrastructure. Yeah, I mean, we're taking the hardware off your hands if you want us to. Yeah, I mean, this is the question depends how far you want to go. If you want to do functional computing, you can do that today. If you want to do VMs, you can do that today. I still see too many people doing VMs in the cloud and thinking it's cutting edge and I'm like, seriously? You know, hopefully the answer question. Anyone else before I hand? Okay. Okay. Oh, sorry. Go on then one more. Which gap in particular? Well, there was a big gap there like USA. Yes. Yeah, we're kind of going where the opportunity is. So we were the first to stick date centers in South America. We're the first to stick date center in Africa. There's more date centers going into Africa. There's more date centers going into South America. You have long lead times on some of these things. So it's not that it actually you have to wait for some of the cables to be laid as well. That's part of the problem. Yeah. It's not just concrete drying out sometimes. Central America has some real issues around connectivity. So does Central Africa. North coast of Africa is a lot easier. You'll see some movement shortly from us there. We're going everywhere. It's just a case of, you know, sometimes you've got to wait occasionally. Okay. There's no, it's not a political thing. We'll stick data centers wherever. We've got them in China. I mean, trust me. But you need like approval from the China government to go there actually. So what I'm normally doing is I'm talking about AI. So my name is Sven Svan Wildermann. I'm a technical solutions professional at Microsoft. And normally I do not have researchers. I even have, I'm having all the universities as my customers. Normally I do not speak to the researchers. I speak to the people wanting the data centers in the organization as well. And often I'm talking about AI and what does that mean? And Brad already explained that's mainly machine learning nowadays. We often speak about weak AI. That's what we are dealing with. There's no strong AI yet. Hopefully it will come. We will see. So, and we made some big things like the last years and the improvements that have been made, probably you made them or your colleagues made them, right? And this is what research. And we reached a lot of human parity with a lot of services. So there's some speech recognition tests where human parity has been achieved as well as in machine translation and conversational questions like this language, there's text understanding stuff. So that's, that's cool. And we do have a lot of own research departments all over the world. The cool thing here is they are not like separated from the rest of the organization. They sit in the engineering and improvements we searched us goes directly to our products. So that's good to know. When Microsoft tries to think about AI, we always divide things in three stuff that's always good for slides. So that's why we do it. And the first thing is AI apps and agents. And now you might think what does that mean is Alexa something that is very smart and an AI app and agent or container, you may have heard of it. Maybe not. Then you could say, yeah, probably. And there are Azure cognitive services and there's an Azure bot service that is the stuff behind it. So what does it mean? Azure cognitive services is mainly it's just a bunch of APIs you can use. And it does something that you are able to do as well, detect something on a picture, translate speech to text, do translation stuff like that. And there's the bot service. What is the bot service? It mainly does one thing for you. If you want to create a bot that works with Facebook and maybe Slack and teams and whatsoever, you don't need to program these channels again and again and again. You want one place to host it, put your logic inside and don't want to need to handle all these different channels. How to get to Telegram channel and make that happen. Yeah, we made it easy for you to do it. And combining both things, you can create quite good apps and agents helping you and your customers and your colleagues to ask questions, get help, hand over like in a situation of a call center, stuff like that. So these are all the cognitive services we are talking about. There are five categories. You can read it on your own, look it up. You can almost every one of those can be tested like totally for free online. Then if you do a lot of calls, it costs some money. But if you just use them like once in a while, they're very cheap, but you don't even cost anything at all. So the second part is knowledge mining. So what does it mean? I also do have a lot of other public or government customers out there and all the insurances as well. And they have a lot of different data sets. And those are not like not everything is stored in database and easy accessible. Sometimes it's even paper that needs to be scanned to pictures or PowerPoint presentations or whatsoever. And we need to access this data to make them available for everyone. Imagine like insurance, you're calling them and you want them to have all the information about you. Maybe you send a picture of your damage and it should be easy to access those data. So there's also some services we are combining. We are talking about Azure Search. That's like one of the cognitive services combined with the OCR detection, language detection and stuff like that. You can make really good productive services out of that. We did one thing to deal with that. We used the John F. Kennedy files, JFK files. They are not confidential anymore. So anyone can access them now. We scanned all these 70,000 documents and tried to access them. So there's a public website out there. You can just access it. It's JFK demo. Just Google it or Bing it or whatever. And then you can go there and just type anything you like. And you can see for instance Oswald, who's probably the guy here called John F. Kennedy. We don't know. And then you see the pictures and all the records where his name is and where the meter text is inside and go there. Good solutions out there. So that's what we mean when we talk about knowledge mining. What is probably most important for you, and I know I just have a couple of minutes left, but that is machine learning. And so machine learning comes in all different flavors. And we have a lot of different tools and sets and stuff we are doing out there. So we have some pre-trained modules like the cognitive services. The bad thing here is you cannot look behind the models we are using there. That's a closed shop. You can use them or not use them. That's cool for commercial customers that want an easy solution to work like something that works now. But they don't need to explain in the details how it works. So probably not the best solution for researchers. But we also host all the different data signed tools and make them accessible in our services. For instance, Databricks. And I don't know who of you knows Spark as an engine. Spark, Spark, Spark, some people. Like, yeah, that tool, mostly everyone nowadays uses to access big data and do big data queries. And Databricks is something you would call Spark Premium. There are some guys that also worked at the Apache Spark stuff. And they made it even better. And now there's a first party service in Azure called Azure Databricks. And what is cool about that one, normally when you're a data scientist working at an institute or even at a company, you want to start today and then you're dealing with your big data cluster installing Spark, getting Hadoop or Clodera running on your cluster and then Spark on top of that. And trying to deal with it. And I did not have seen one person now that did it right at the first try. So it's very, very hard to configure those stuff. And Databricks makes it easy for you to just go on what you really want to do. You want to analyze your data, right? And you can create your cluster in just 10 minutes. You can just put some parameters in how many working nodes do you want, how big should they be. And you get a head node and a few worker nodes. And then you are done, you can do your work. So it's quite nice. There's also an Azure Machine Learning service that combines like all the training and testing. I'm going to that in the next slide. And so Microsoft is not the Microsoft we have been like 10 years ago. We do have our own framework. But to be honest, no one used that. So we don't really stick to that one. People out there, they really use PyTorch or TensorFlow and Skykit Learn. And so what we are doing more and more is focusing on that once and make it easy to run on that in our cloud to make it easy for customers to do the data science, to do the machine learning, because that's something all the institutes and researchers and companies want to do nowadays. And we try to stick what's out there and what's out in the market and not try to reinvent the wheel, so to say. And Brett already talked about the powerful infrastructure we have behind the FPGAs, GPUs and CPUs out there. So there's one thing that's, if you think about machine learning, that's a big problem because the typical data scientists I met, they normally, they think they're done when they found a good model with like 98% precision and we call. So and then they think they're done, but that's not in production, right? So it's easy, it's not easy to go beyond that one and put that into production and deploy the model and monitor the model as well. And there are not many tools out there, MLflow from the open source project, I guess, is another tool that does it. But there are not many tools like considering the whole workflow and there's something out there that's called Azure DevOps. It's mainly made for developers. Three tools, there's another one. Three minutes left, okay. Okay. And Azure DevOps, we bought a, got an extension out there for Azure DevOps so that you can do all the machine learning operations out there with Azure DevOps as well. So training a model, validating a model, deploying model, your model and monitoring it. And you probably need to do it again once a while because your data normally changes, your customer change and stuff like that. And yeah, there's a tool for it now. So try it out. If you have any questions, let me know. And if you think that's, that's not good at all or that some bugs need to be fixed, it's good for us to get the feedback because that's like something we want to do. We want to be better and like do valuable stuff for you. It's not just like competing against others and just having the same feature set that others have, but also having like nice software. And I guess Visual Studio Code is one of the IDEs. Some of you might know it runs on Windows, Linux and Mac and a lot of developers love it because we try to do what developers want to have and not just sell another product with the Microsoft logo in it. So one point and we are also already points out there is just, it's an open platform. So if you do not have the resources on your own and you want to do something and it might not work with the Institute or you want to be fast and you need cloud infrastructure to do it, one of the options is Microsoft. We are doing that and this is our main focus nowadays to work and stuff like that. Windows is still out there, but that's like not the main bet we're doing nowadays. It's really it's Azure and all the things working there and AI and machine learning is really what we are concentrating on. And I would be happy if one of you is just trying it out and giving it a try and let me know even if it doesn't work, just let me know just why it does not work for you. I'm also having some customers doing genomic data and the genomic data and they want to prepare them and get fast on those data sets and often they need to plan like in their HPC cluster when to do those stuff and that's like one slot in a month or so, but they want to like need to be fast in the research paper. So that's one of the things where we can help and some projects already showed that we are able to help and you don't need to change your code away from TensorFlow or PyTorch to any Microsoft product that's not needed anymore. May you have thought so. So thanks a lot for having me here. If there are any questions, just get them now. Thank you. Thanks very much, Senn and Brad. I'm sure you or your colleagues will be around at the booth for any questions today and also your colleagues will be here for the whole conference. With this I think I'll hand over to, unless are there any questions? There's one in the back, right? Right, the question was are there any institutions in Germany which have established a policy for using these kinds of services? So I knew that we have some institute already working with it, but I'm not sure if they have like, we have some institute already working with cloud software on it. I'm not sure if I am allowed to put out the names. I need to check with my colleague and I'm not sure if they have like a policy out of there, but I'm sure there are a lot of institutes or there are some institutes that are already doing that stuff and we also even having a health insurance which already has been working on the cloud and the social data is in Germany, the most protected data that we have and also they are currently being processed in the cloud. Often it's like that that you then of course key them in, but in the meantime there are solutions to do the keyings themselves, so that you can not only do it when transporting and when the storage, but also practically not only in transport and at rest, but also at compute, which can be keyed in by getting the code near the CPU. So we can talk about it. There are definitely opportunities. Often it is a political discussion that must be met at the institute.
 I have this weird job title,  which says Director of Higher Education Research,  and everyone suddenly thinks that I work for  Microsoft Research and I'm not nearly  bright enough to work for Microsoft Research.  My role at Microsoft is to try to help  the research community take advantage of  our Cloud Services of a software stack,  which is exactly what you guys are doing.  One of the reasons that we are working very closely with  RSE Communities globally is we're trying to do the same thing.  I'll say right up at the front,  yeah, I know I work for Microsoft,  but I don't really care about that bit because my colleagues,  AWS who are somewhere,  one of them used to work with me at Microsoft,  he's up the back.  We're all in the same boat and trying to  achieve the same things at the moment.  So we have much more in common than we do against each other.  There's three things I'm going to try and do in the next 45 minutes.  One is tell you stuff that you already know,  but I think maybe it'll be ammunition for some of you to  help in the process of building  the RSE community with people outside of this room.  The second is talk about some of the things that we're doing with  RSE Communities around the globe,  and then Sven's going to do a bit of a deeper dive into our AI platform,  and just cruise along to give you a bit of a view about other things that you can do.  Hopefully, it'll be interesting to you.  Last thing, I'm a Microsofty,  so I have tons of slides.  I've seen them all.  They're really good, but I don't need to see them.  So I'm happy to take questions.  It's much more important to me that you get  questions answered than I get through my slides.  It's probably more important that Sven gets through his slides,  but I really don't care.  So if you've got a question at some point,  stick your hand up, makes it way more interesting to me,  because I'm on Seattle time still,  and it's just that afternoon bit where I'm starting to fall asleep.  So I'll go into autopilot on the slides if you're not careful.  So it's your choice.  You can ask interesting questions or I'll go into autopilot mode.  There you go. I warned you, as they say.  I'm lying on this slide.  First slide, I'm lying already.  Why am I lying?  Because the we doesn't apply to us.  The intersection that everyone's out of the moment is the move to Cloud computing.  Whilst there's been a lot of movement,  we're basically not even scratching the surface yet.  When I say we are not even scratching the surface,  actually I mean you and the institutions that you work with.  We, i.e. Microsoft and in the case of Amazon,  are we don't have a concept anymore of on-prem stuff.  We don't have a set of Cloud data centers that we develop against,  and then some local data centers that we use for something else.  We just have this set over here.  We closed our last kind of on-campus data center about three years ago.  So we're not an intersection.  We're way past that intersection.  But you guys still are.  A lot of your institutions are not even at that point yet.  The amount of, I'll be provocative,  the amount of new HPC on-prem things that I see coming out, tenders,  unlike seriously, what year are we in?  I still see people doing this and they're going to keep doing it for a while.  When you get evolution,  it doesn't mean that everyone evolves at the same rate.  But actually, you've just got to look where the graphs are going.  It's disappointing to me in a way that people don't  plot the graphs ahead far enough.  It's disappointing to me out of not you as individuals,  but some of the institutions that I deal with,  because there are really bright people there whose job is to  plot graphs for everyone else in the world to say,  this is what's happening.  Yet there seems to be some intransigence about looking at their own stuff.  So there is an intersection.  We're not at it, we're past it,  but we still need to get people through it and that's going to take a while.  You guys all know what's going on.  So this is the stuff that you know.  Big data cloud processing, machine learning.  I hate the brackets AI.  Yeah. It's machine learning still to me.  We'll get to AI, I'm sure at some point,  but it's machine learning.  But it's really when you can combine that together,  it feels almost like AI.  To the man in the street, it may as well be AI.  People in this room know it's machine learning.  That's changing the way the research is being done.  Yeah. Again, this isn't new stuff.  We've been going through this change for a long time.  Fourth paradigm was written a long time ago.  If you come a bit more up to speed,  you've got people like Tony Hay who used to work for us saying,  you really have to look at the rate of acceleration,  and you then have to look at those graphs again to  see how this is going to affect everybody.  It's interesting to look back slightly and say,  wow, weren't those numbers really small?  750 megs seemed like a lot a while ago.  I was reading, Apple did their new launch yesterday,  and you can put 1.5.  I'm going to get this wrong because it sounds wrong.  You put 1.5, I want to say terabits of RAM into the thing.  I mean, I'm like, really?  The numbers are just crazy.  The LHC is still spitting out  more data than anyone knows what to do with.  It's a good job we throw two orders of magnitude away  before you try and do anything with it.  But really, it would be nice to look at those two orders of magnitude.  Today, we're led on a path that says,  go look at this tiny little spot in the spectrum  because we think there's something there interesting.  We have data for all this other bit,  but we have to throw that away to look at this single spot.  It would be nice to run some machine learning  against the rest of it to say,  wow, that's interesting.  There's something over here that the theoreticians  haven't actually discovered yet, thought about.  That's what's not happening.  Then when we get into the square kilometer array,  things get really wild.  In fact, they get so wild that people are saying,  how are we really going to cope with this data?  Big data centers is probably the answer.  So it's always nice to show you pictures.  Here's a small data center.  You can tell the scale because  that's a car at the bottom running along a road.  This is actually two,  you're looking at three generations of data center in here.  Let me get it the right way around.  The right-hand side,  the left-hand side as you look at it,  is one of our C-series data centers.  Then you can see evolve into these modular blobs.  Then at the top,  you can see this backbone piece,  which is the next series.  So you can see as we build these things out,  we're constantly evolving them.  But actually, this is quite an old one.  If we look at the top of this,  the top bit of buildings is the bit in the previous picture.  This is quite big.  This is about two miles long and  about three-quarters of a mile wide.  This is one of dozens that are being built.  The biggest challenge to us today,  the biggest thing that slows us down,  is how fast concrete will set.  You still have to pour a concrete base to put something on.  Trust me, people have been looking at doing,  what could we do that's quicker than that?  Then you fill these things.  We'll get onto what we fill them with in a minute.  But the point of putting this up is just to  re-emphasize the scale thing.  We all work in an industry that is using ever more amounts of data,  ever more amounts of CPU,  ever more amounts of bandwidth.  So you're pretty close to the coal face.  But actually, you still have to step back occasionally and  just remind yourself,  this is where we're going and this is an accelerating path.  So if we're building these things today and  sinking things off the Orkneys underwater like we did last year,  what you have to look at is where we're going to be in three or four or five years time.  UKRI doing a 10-year study,  I'm like good luck if you can predict 10 years out at the moment.  I mean, you're doing really well.  Then we're not just doing this in one place,  obviously, we have a few of these scattered around.  So I think this slide normally needs updating when I do it,  but I think I'm still accurate that there's 54 current regions.  There aren't 54 current regions because how nice it is for  us to put a region in Brazil so that Brazilian software developers can use local resources.  No, the reason we have a region in Brazil is that European companies sell products into Brazil,  and they want that connected thing, car,  coffee maker, freezer, whatever it is.  We used to laugh when we said that and you can buy all of those things today.  They want that to work locally against a local data center,  and they need it to work locally because you may have  restrictions in that country that say,  you can dial home but not with all the data,  only with some of it.  That's why we have data centers in China and India and South America,  and South Africa, and go on and on and on.  Yes, the ultimate extension of this is one at the bottom of your garden,  because we're literally rolling these things.  I think France was the last set that we went live on.  I don't know, I lie.  UAE was the last set that we went live on.  This is happening more and more and more and more.  Again, think about the scale and think about what that means to you,  and think about what that means for the resources.  The same with the network.  We're not just using the networks that exist,  we're buying, building, and laying the new networks that are out there,  because you have to when you're moving data at this kind of scale.  When you're failing over between regions, if necessary,  because it's not just failing over between those data centers,  or failing over between a twin set of data centers in one region  and another one in the same US region,  it's about failing an entire region over to another region if you need to do so.  You have a tsunami in Japan, you fail everything over to the US.  That's what you have to do, assuming your customers say that it's OK for you to fail over to the US.  A lot of people wouldn't be happy about that.  So what do we put in them?  Everything that you would imagine, frankly, proper HPCs within Finneband,  FPGA systems as well.  Probably only 18 months ago, I used to put this slide up and talk about FPGA systems.  Even an audience like yourselves would say, wow, that's pretty esoteric.  There's not many people doing FPGA programming for the systems that they're working on.  Even in the last 18 months, this is becoming more and more and more mainstream.  The guys at CERN are using these,  because it's the only way for them to get the throughput that they need to do some of the work that they're going to do.  Equally, I've been working with a research group in the Netherlands who are doing neonatal work  and they were doing genome processing.  And they've really needed to get their processing times down into the couple of hours margin  because you don't have that amount of time with neonatal patients.  You just don't have hours and hours and hours or days to wait.  You have to do it quickly.  So even in the last 18 months, what was really esoteric 18 months ago  is starting to be more mainstream.  Well, common, let's put it that way.  And then if you really want to go the whole hog, you can stick crazy systems,  or I suppose I should say HP systems these days, in as well.  And again, the interesting thing about this is most organizations in most parts of the world  just can't afford to put that kind of infrastructure into their own organizations.  But now they don't have to.  One of the things that we're doing here, and again, not just us, my colleagues at AWS are doing exactly the same thing,  is that you're democratizing the capability to do research.  You're saying to someone in a research institute in South Africa,  you can have access to all of the compute you could ever want without any upfront payment at all.  And you can buy it by the minute if you'd like to do that.  And you can buy the kind of scale systems that you used to dream about.  And so you can attract researchers to your institution, not based on the capabilities of that institution,  but based on what the rest of the institution can offer them, or great surfing if you're near Durban.  So this is changing the nature of some of the institutions that you work with,  and it's going to change the nature of how things get done.  But it's a democratization process, and that's a good thing in my view.  I'll stick this slide up. All these slides are available, so you're not meant to read them in brief,  because everyone is rolling out these new systems literally weekly, which is, again, another point.  It's like we and our friends at Amazon, and if they were here, our friends at Google,  are investing in the hardware in a way that nobody else is.  So it means that you're getting access to new systems, basically as fast as they're available,  which means that you are not compelled to buy something and sweat the assets over a number of years.  We have to do that, that's our problem, and we'll continue to do that, but it stops becoming your problem.  And this is just the latest bunch that have rolled out. Now, okay, the hardware is great.  What about the stuff that runs on it? Because, let's face it, I've been a Microsoft a long time,  convicted monopolist, lived through many years of DOJ trials.  Not a pleasant thing, frankly, as an employee. I like to think we've learned from it.  Actually, I think we've really learned from it. And when I look at how we work now,  it's kind of weird to me that the world's largest installed base of Windows systems is at AWS,  and we run more Linux clusters than anybody else. And we make more contributions to Linux,  not just Linux, actually, to open source repositories than any other,  quote, proprietary software company out there. So it's not the Microsoft I grew up with,  that's for sure. I like to think we've evolved past that, but I have to make the point,  because I know most people still don't see that. And it's not just a one layer. We have to do it at  all layers to be able to do this. We have to really make sure that everything that you  want to do, you can just do. And that's a complete change from us. We have to make sure we trust it  as well. So every certification that's out there, we've got to make sure that it's available,  because a lot of the organizations that are doing the research with you, or funding the research,  need us to meet these requirements. So you saw us roll out FedRAMP to every region in  the US last week, because surprise, surprise, the US government would quite like us to be  compliant with that. So we have to be open and we have to be compliant in order to be trusted.  And that's stuff that you know, but you can use with others who don't know. So what about you guys?  Why are we here and what are we doing? Well, the first thing is really,  when we thought about this a few years ago, the classic Microsoft response would have been,  we'll go and set up a Microsoft community. We'll do this Microsoft thing and then we'll invite  people to join it. Yeah, and some of you probably would have joined. Thought, okay,  there's probably free beer, so I'll go along. And it would have been okay. It wouldn't have been  okay. It wouldn't have been great. And I persuaded a lot of people at Microsoft that that is not  the way that we should do things. Actually, what we should do is look at the way that the UK had  built its RSC community and then work with other organizations around the world to kind of go to  you guys to turn up where you are on your terms, which is why we're here speaking at your conference,  not a Microsoft thing and why we're working with the South Africans, the Nordics, the Dutch,  the US guys to try and get the same approach going there. I have to tell you, it's harder.  Yeah, it's a lot harder and some of my senior managers kind of look at me and think I'm a bit  mad, but I think it's the right way to do things. The other thing that it does is it means you don't  have to worry about us losing focus because like most software companies, the focus shifts  dramatically sometimes. If we had set something up, the danger is at some point somebody decides  to defund it and then you guys go, what about us? Yeah, you don't have to worry. It's your thing.  You never have to worry about that with us. You may decide you want a different sponsor  next year. Great. That's a pretty minor thing to have to worry about. What you don't have to worry  about is us suddenly disappearing in your organization going up in flames. We want to come to where you  are and we're doing that by investing in the community in a lot of different ways. We're putting  a role that we call a research engagement manager. We've got one in the UK at Imperial,  we've got one in Australia, in Melbourne, one in Hong Kong today. We've got a different setup in  the US because we're working with Indiana University to roll out a program on our behalf there,  but these people are not Microsoft people. They're actually researchers or their research  associates or their RSEs. Again, what we're trying to do is to build bridges into the community and  to say, tell us what we need to do. Tell us how we need to work. Tell us what you need for you to  be successful because fundamentally, if you're successful, we will be successful. It's not hard.  It's sometimes hard for me to get my management to understand that, but it's not hard. It's just  the way we should do things. Next year, you're going to see us do a lot more in terms of,  I use this terrible training word here, training for the research community. Actually, what that  really means is something that we did two weeks ago at Imperial. In fact, we did a mixer at Imperial  and our reactor centre in London where we got a group of interested people together, some of whom  are RSEs, and said, tell us the stuff we need to fix. Tell us the problems you run into. We ran  through a bunch of scenarios with them that they had brought to us, not that we had started with.  Then we're looking at, okay, what were the roadblocks that people here are? There's this issue in  AAD provisioning that doesn't set things up correctly. If you try and then use it as an RSE,  if you're using AAD, you don't get to provision the services that you want without making some  basic changes. We need to change that. Great. Our engineering people love that. It's like,  this is what you need to fix to allow people to use your service more. Then we're publishing all  that information out onto GitHub and out into our technical documentation on docs.com so that it's  there for anyone to take advantage of. Again, it's not our stuff. It's your stuff. We're just  providing a place for someone to be able to host it and people to be able to go and find it and  link to it and work with it in the future. Hopefully, that's what we call training.  Because if I don't use that term internally, people really don't get. Most of the people I  work with have no concept of what you guys do anyway. Just let me use the training word on here.  Don't take it to heart too much. The other thing that we're doing is we're trying to integrate  all of our technical documentation and all of the GitHub repositories that we and you create  together inside the main Azure portal. You'll see this roll out at the end of this year as well.  This means it's really easy to go to one place, start a project, understand where you need to go  in order to get the documentation to help you with that project. If you're working on something new,  how easy it is to be able to push back into the community so that everyone else can use whatever  it is that you're working on. If we can make that cycle easier, then hopefully we'll help you guys  in your work in trying to help the researchers get their work done. Our ultimate goal is just to try  to help researchers get their research done faster, get their publications published quicker,  and make your lives a bit easier in doing that. And to try and help you guys stay up to speed.  Because as people said earlier, this stuff is advancing faster and faster and faster.  The biggest problem is actually staying up to date, making sure that you're taking or  able to take advantage of the latest developments and the latest piece of things.  Software is really eating the world. Every organization really is a software company these  days, which is kind of nice. Certainly should be good if you're in the software business.  Every organization is depending on people like you more and more and more. It's just that not  all of them realize that yet. I like to think that we do and that we're going to try and invest in  this community and in building these communities more and more and more in the coming years.  But at least you understand what we're doing and why we're doing and the acceleration that's going  on in that. I'm going to hand over to my colleague Sven who's going to talk you through our AI platform  just before I do that. And just to check you're awake.  Any questions at this point? Yes.  Yeah, fundamentally. The question was what's the business case? How do I get funded?  Yeah, Azure is what the company is betting on. I mean, you're still here. People talk about windows  and office and stuff like that. But frankly, it's Azure services or bust.  It's just infrastructure like your services working on the infrastructure.  Yeah, I mean, we're taking the hardware off your hands if you want us to.  Yeah, I mean, this is the question depends how far you want to go. If you want to do functional  computing, you can do that today. If you want to do VMs, you can do that today.  I still see too many people doing VMs in the cloud and thinking it's cutting edge and I'm like,  seriously? You know, hopefully the answer question. Anyone else before I hand? Okay.  Okay. Oh, sorry. Go on then one more.  Which gap in particular?  Well, there was a big gap there like USA. Yes.  Yeah, we're kind of going where the opportunity is. So we were the first to stick date centers in  South America. We're the first to stick date center in Africa. There's more date centers going  into Africa. There's more date centers going into South America. You have long lead times on some of  these things. So it's not that it actually you have to wait for some of the cables to be laid as  well. That's part of the problem. Yeah. It's not just concrete drying out sometimes. Central America  has some real issues around connectivity. So does Central Africa. North coast of Africa is a lot  easier. You'll see some movement shortly from us there. We're going everywhere. It's just a case of,  you know, sometimes you've got to wait occasionally. Okay. There's no, it's not a political thing.  We'll stick data centers wherever. We've got them in China. I mean, trust me.  But you need like approval from the China government to go there actually. So what I'm  normally doing is I'm talking about AI. So my name is Sven Svan Wildermann. I'm a technical  solutions professional at Microsoft. And normally I do not have researchers. I even have, I'm having  all the universities as my customers. Normally I do not speak to the researchers. I speak to the  people wanting the data centers in the organization as well. And often I'm talking about AI and what  does that mean? And Brad already explained that's mainly machine learning nowadays. We often speak  about weak AI. That's what we are dealing with. There's no strong AI yet. Hopefully it will come.  We will see. So, and we made some big things like the last years and the improvements that have been  made, probably you made them or your colleagues made them, right? And this is what research.  And we reached a lot of human parity with a lot of services. So there's some speech recognition  tests where human parity has been achieved as well as in machine translation and conversational  questions like this language, there's text understanding stuff. So that's, that's cool.  And we do have a lot of own research departments all over the world. The cool thing here is they  are not like separated from the rest of the organization. They sit in the engineering  and improvements we searched us goes directly to our products. So that's good to know.  When Microsoft tries to think about AI, we always divide things in three stuff that's always good  for slides. So that's why we do it. And the first thing is AI apps and agents. And now you might  think what does that mean is Alexa something that is very smart and an AI app and agent or  container, you may have heard of it. Maybe not. Then you could say, yeah, probably. And there are  Azure cognitive services and there's an Azure bot service that is the stuff behind it. So what  does it mean? Azure cognitive services is mainly it's just a bunch of APIs you can use. And it does  something that you are able to do as well, detect something on a picture, translate speech to text,  do translation stuff like that. And there's the bot service. What is the bot service? It mainly  does one thing for you. If you want to create a bot that works with Facebook and maybe Slack and  teams and whatsoever, you don't need to program these channels again and again and again. You want  one place to host it, put your logic inside and don't want to need to handle all these different  channels. How to get to Telegram channel and make that happen. Yeah, we made it easy for you to do  it. And combining both things, you can create quite good apps and agents helping you and your  customers and your colleagues to ask questions, get help, hand over like in a situation of a  call center, stuff like that. So these are all the cognitive services we are talking about. There  are five categories. You can read it on your own, look it up. You can almost every one of those can  be tested like totally for free online. Then if you do a lot of calls, it costs some money. But  if you just use them like once in a while, they're very cheap, but you don't even cost anything at  all. So the second part is knowledge mining. So what does it mean? I also do have a lot of other  public or government customers out there and all the insurances as well. And they have a lot of  different data sets. And those are not like not everything is stored in database and easy accessible.  Sometimes it's even paper that needs to be scanned to pictures or PowerPoint presentations or  whatsoever. And we need to access this data to make them available for everyone. Imagine like  insurance, you're calling them and you want them to have all the information about you. Maybe you  send a picture of your damage and it should be easy to access those data. So there's also  some services we are combining. We are talking about Azure Search. That's like one of the cognitive  services combined with the OCR detection, language detection and stuff like that. You can make really  good productive services out of that. We did one thing to deal with that. We used the John F. Kennedy  files, JFK files. They are not confidential anymore. So anyone can access them now. We scanned all  these 70,000 documents and tried to access them. So there's a public website out there. You can  just access it. It's JFK demo. Just Google it or Bing it or whatever. And then you can go there  and just type anything you like. And you can see for instance Oswald, who's probably the guy  here called John F. Kennedy. We don't know. And then you see the pictures and all the records  where his name is and where the meter text is inside and go there. Good solutions out there.  So that's what we mean when we talk about knowledge mining. What is probably most important for you,  and I know I just have a couple of minutes left, but that is machine learning. And so machine learning  comes in all different flavors. And we have a lot of different tools and sets and stuff we are doing  out there. So we have some pre-trained modules like the cognitive services. The bad thing here is  you cannot look behind the models we are using there. That's a closed shop. You can use them  or not use them. That's cool for commercial customers that want an easy solution to work  like something that works now. But they don't need to explain in the details how it works.  So probably not the best solution for researchers. But we also host all the different data  signed tools and make them accessible in our services. For instance, Databricks. And I don't  know who of you knows Spark as an engine. Spark, Spark, Spark, some people. Like, yeah, that tool,  mostly everyone nowadays uses to access big data and do big data queries. And Databricks is something  you would call Spark Premium. There are some guys that also worked at the Apache Spark stuff. And  they made it even better. And now there's a first party service in Azure called Azure Databricks.  And what is cool about that one, normally when you're a data scientist working at an institute or  even at a company, you want to start today and then you're dealing with your big data cluster  installing Spark, getting Hadoop or Clodera running on your cluster and then Spark on top of that.  And trying to deal with it. And I did not have seen one person now that did it right at the first  try. So it's very, very hard to configure those stuff. And Databricks makes it easy for you to  just go on what you really want to do. You want to analyze your data, right? And you can create  your cluster in just 10 minutes. You can just put some parameters in how many working nodes do you  want, how big should they be. And you get a head node and a few worker nodes. And then you are done,  you can do your work. So it's quite nice. There's also an Azure Machine Learning service that combines  like all the training and testing. I'm going to that in the next slide. And so Microsoft is not  the Microsoft we have been like 10 years ago. We do have our own framework. But to be honest,  no one used that. So we don't really stick to that one. People out there, they really use PyTorch  or TensorFlow and Skykit Learn. And so what we are doing more and more is focusing on that once  and make it easy to run on that in our cloud to make it easy for customers to do the data science,  to do the machine learning, because that's something all the institutes and researchers and  companies want to do nowadays. And we try to stick what's out there and what's out in the market  and not try to reinvent the wheel, so to say. And Brett already talked about the powerful  infrastructure we have behind the FPGAs, GPUs and CPUs out there. So there's one thing that's,  if you think about machine learning, that's a big problem because the typical data scientists I met,  they normally, they think they're done when they found a good model with like 98% precision  and we call. So and then they think they're done, but that's not in production, right? So it's easy,  it's not easy to go beyond that one and put that into production and deploy the model and  monitor the model as well. And there are not many tools out there, MLflow from the open source  project, I guess, is another tool that does it. But there are not many tools like considering the  whole workflow and there's something out there that's called Azure DevOps. It's mainly made for  developers. Three tools, there's another one. Three minutes left, okay. Okay. And Azure DevOps,  we bought a, got an extension out there for Azure DevOps so that you can do all the machine  learning operations out there with Azure DevOps as well. So training a model, validating a model,  deploying model, your model and monitoring it. And you probably need to do it again once a while  because your data normally changes, your customer change and stuff like that. And yeah, there's  a tool for it now. So try it out. If you have any questions, let me know. And if you think that's,  that's not good at all or that some bugs need to be fixed, it's good for us to get the feedback  because that's like something we want to do. We want to be better and like do valuable stuff for  you. It's not just like competing against others and just having the same feature set that others  have, but also having like nice software. And I guess Visual Studio Code is one of the  IDEs. Some of you might know it runs on Windows, Linux and Mac and a lot of developers love it  because we try to do what developers want to have and not just sell another product with  the Microsoft logo in it. So one point and we are also already points out there is just,  it's an open platform. So if you do not have the resources on your own and you want to do  something and it might not work with the Institute or you want to be fast and you need  cloud infrastructure to do it, one of the options is Microsoft. We are doing that and this is our  main focus nowadays to work and stuff like that. Windows is still out there, but that's like not  the main bet we're doing nowadays. It's really it's Azure and all the things working there and AI  and machine learning is really what we are concentrating on. And I would be happy if one of  you is just trying it out and giving it a try and let me know even if it doesn't work, just let  me know just why it does not work for you. I'm also having some customers doing genomic data  and the genomic data and they want to prepare them and get fast on those data sets and often they  need to plan like in their HPC cluster when to do those stuff and that's like one slot in a month  or so, but they want to like need to be fast in the research paper. So that's one of the things  where we can help and some projects already showed that we are able to help and you don't need to  change your code away from TensorFlow or PyTorch to any Microsoft product that's not needed anymore.  May you have thought so. So thanks a lot for having me here. If there are any questions, just  get them now. Thank you.  Thanks very much, Senn and Brad. I'm sure you or your colleagues will be around  at the booth for any questions today and also your colleagues will be here for the whole conference.  With this I think I'll hand over to, unless are there any questions? There's one in the back, right?  Right, the question was are there any institutions in Germany  which have established a policy for using these kinds of services?  So I knew that we have some institute already working with it, but I'm not sure if they have like,  we have some institute already working with cloud software on it. I'm not sure if I am allowed to  put out the names. I need to check with my colleague and I'm not sure if they have like a policy out of  there, but I'm sure there are a lot of institutes or there are some institutes that are already doing  that stuff and we also even having a health insurance which already has been working on  the cloud and the social data is in Germany, the most protected data that we have and  also they are currently being processed in the cloud. Often it's like that that you  then of course key them in, but in the meantime there are solutions to do the keyings themselves,  so that you can not only do it when transporting and when the storage, but also practically not  only in transport and at rest, but also at compute, which can be keyed in by getting the code  near the CPU. So we can talk about it. There are definitely opportunities. Often it is a  political discussion that must be met at the institute.
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