Hey, guys, how's everybody doing? So for those of you who don't know, I'm Mike Vizard, and I'll make a little confession. I'm what's known as a conference rat. Every spring, every fall, I go to IT conferences every week for the last 30 years. And so I would just like to let you know, though, that as I sat here for the last two days, that the divide between business and IT has never been wider. We've been talking about it for 20 years now, and I'm going to argue that this panel is going to be one of the more important panels you're ever going to hear on this subject. And so with that, though, I don't think most people know exactly what we mean by low code. So I'm going to open up with Johan, kind of giving us a little tutorial about what low code is, because it's not what you think it is. It's not a similar, and it's not about a place where my friends are. It's about a different way of thinking. Thanks, Mike. So low codes, I think, is best explained by taking one step back. So why are we all here? That's because we want to win in a software-driven world. And that's why we are interested or in Cloud Foundry or using Cloud Foundry. And if you think about Cloud Foundry and the advantages of Cloud Foundry, basically two key words pop into my mind. Cloud Foundry is all about abstraction and automation. It's about abstracting away from lower-level infrastructure layers and making sure we move from an infrastructure-oriented world to an absentric world. So what Cloud Foundry is doing is applying abstraction and automation to application deployment and operations. And basically, low code is doing the exact same thing, abstraction and automation, but then to application development. And evolution. So low code is all about abstracting away from lower-level programming and making sure you can use visual models to build your applications. And that means that you can use it's faster. So it's up to 10 times faster than traditional programming. But it's also inviting broader audience to developing applications and being part of the application development process. So with that, I was talking about the divide between business and IT. I just want to put a thought in your head for a minute. Business people care about one thing. It's called the number. And the number is the X number of things that have to be sold by Y amount of time at Z profit. And it's all they're ever thinking about. They get up in the morning and they think about that number. They go to bed at night, they think about that number. They just throw spouses and they think about that number.  So when you guys show up and go, you know, we're going to cut the amount of time that it takes to develop an app from two years to 12, you know, they forgot what you said. I mean, two years to 12 months, they forgot what you said even before you left their room because it has nothing to do with the number in their head. And so they're thinking about the world differently than you think about it. So with that in mind, I want each panelist to kind of introduce themselves and kind of talk about how you came to Mendex and how it kind of changed the culture in your organization. Let's start with Olu. So I'm Olu Brown. I work as a director at MIT. I run an application platforms team. So we've been using Mendex, I would say, for around two and a half years. John Charles, the CIO for MIT came in and wanted to have kind of a more data-driven approach to application development. And Mendex was identified as a platform that we could actually use to prototype applications and to kind of manage to kind of that low-code infrastructure framework and application layer to really figure out, does it work? I think short answer, it has worked. We have about 15 applications that we're running at MIT, but it's changed kind of the paradigm of how we develop these applications because like you said, things can take a year to two years to develop something. We generally turn stuff around in three to four months now. Cool. Jonathan? I'm Jonathan Boshay. I'm a partner with the Solomon Group for the Orla, Louisiana. Produced very large events and sporting events and entertainment events. And the way we came to Mendex is we were, we have some pretty unique processes in our industry that we have to go through. And we were looking for a piece of software to help us manage large-scale events. And we quickly found that there was nothing out there that fit our process. And we didn't want to buy an off-shelf piece of software that made us conform to the process that the software was written for. So we decided we could create our own. And by create our own event, let's go find a software company. So we went down that road for about a year and we realized that the expense and the complication of just explaining our process to someone else who isn't in our business was gonna take away too long, cumbersome and expensive. So we started looking around and said, okay, maybe this is a filemaker thing. What is this? What can we use? And we came across Mendex. And we started by building out the ERP program of speaking about it, which we use in-house and it's been live for about a year and a half now. And basically, the processes we wanted to use in our business, we developed a system for Mendex and not the other way around of trying to buy a piece of software and make our process of that. So that was hugely important to us. And it's been a great return on our investment with Mendex. Since then, we've even gone further and realized that it was Mendex for our clients. So we have about 20 applications running right now, three of them are internal and the rest are for our clients, larger than the events that you've probably been to. Cool. Beth Ann? Hey, great. My name is Beth Ann Berksmark. I'm the Assistant Vice President for Academic Systems and Chief Enterprise Architect at Georgetown University. And I spent a majority of my time really focusing on how can we across the entire campus break down aging legacy IT that's locked in silos, moving into modern platforms in a way that embraces innovation rather than impede innovation. And if you've ever had any connection with academia, you don't always think of academia as, you know, cutting edge or a little bit more of the glacier. But I think that that's changing now with options like Mendex and low code and cloud. We adopted Mendex in a specific, our research and regulatory domain. And we had very traditional IT problem. We had five or six different applications. One was failing at scale. Four didn't really meet the needs of individual campuses because in higher ed, not only do we have very specific processes that are unique, each campus will define that process independently. So it's almost an exponential increase in the number of legacy applications that can exist at 91 point in time. And traditionally, our solution path would be, look for a software package, which is not going to meet half the needs. And then that will spur more shadow IT. Or I could hire a consultant that would build me a very expensive custom application that I would never be able to change. So it would be out of date within 12 hours of launch. We could build it ourselves, but that would take two years. And at that point, half the developers would have gone, graduated and gone someplace else and no one would know their code. So Mendex really offered us several options. One, speed to launch and to accelerate, follow the cloud first principle. But most importantly is what we found in the low code environment was the ability for reuse and repeatability. And we use this as an opportunity not to replace five disparate apps, but to build one app so we could combine where the campuses has already agreed on similar business processes, but then build in the variation for other areas. And the benefit that that brought to our business was not only solving the problem, driving down some of the costs, speed to launch, but we improved the experience for our researchers who in the past, you might be one researcher, you've got to go to all of these different places. I mean, at one point, we even had seven websites to tell you which way to go. And the instructions were not the same every time. So giving them one simple intuitive experience that we can also innovate and change over time has been phenomenal. And we really see this as an excellent tool for dealing with, you know, I think we've got like 800 of these apps out there. So down the road. So have you guys kind of changed the, I don't like the word, but paradigm in terms of what a developer is. And I asked the question because everybody's thinking about, you know, there's a developer shortage and I can't do this and I can't do that. And then we wind up prioritizing stuff. So, you know, in your shops, have you seen more developers or people who aren't really traditional developers? They might even be business people or whatever, actually starting to write applications. I don't, maybe not code per se, but they're building apps. Yeah, I mean, are you guys seeing that? Yeah, I mean, I think at MIT, our menics development team, none of them were traditional software developers. We had QA engineers, a technical project manager, and a senior business analyst who took up Mendix. I think the biggest part that Mendix brings is, if you have analytical and logical kind of structure, you can build the applications without having to dig down, you know, into the kind of low application program in area. Yeah, I think we found a similar thing in our business. You know, the nature of our business with producing large events, there's a lot of technical aspects to that, sound lighting, video, and all those types of things. So we were able to take some of those folks who were technically savvy and understood structure and electronics and some of those things that kind of crossover into the code world and let them loose on Mendix and they were able to quickly, none of these people had ever made an app before. They touched on the next application. So they were able to quickly kind of figure it out. Now we're a year plus into it and I would say that if I teach a class on Mendix, that those are the only things. We've seen similar crossovers. So when we first started using Mendix, we definitely built the team with some traditional Java programmers. But then, you know, some of our best Mendix developers have been from our DBA team and joining in into this agile team environment. I think that where the role for developer is changing and this is true of every area in the IT stack. So the last 10 years, we forced our IT professionals into these very tightly defined areas. So I'm the VM person. I'm the block storage person. I'm the developer of this language. I'm the architect in this area. And the new technologies really require you to not only think across the IT stack, but to understand the business and the user experience. So I think where our developers are starting to shift their mindset is not just thinking that I'm going to code to spec, but I'm actually going to think about the overall customer experiences. And I'll use speed as a good example. Like speed is the unspoken requirement that will tank your application that no stakeholder will ever tell you is important. But as developers are starting to realize that, and I think that's helping to traverse this gap of understanding the dynamic infrastructure in terms of understanding what gets adopted and what doesn't. Yeah, and for us, it seemed like it was far easier for us to find someone who knew what the process should be. Figure out how to do the programming in the other way around. The beauty, of course, is that if you have... I mean, you started this panel by talking about the gap between business and IT. And the best way to solve that is to make sure that the same people are all involved in application development, not only in giving feedback, but also in designing part of the application, the business processes, the UI of that application. So basically, if you lower the barrier of how you build applications, you could say that that is democratizing application development because more people are able to build applications and be part of that whole process. Yeah, I think participation is actually really the key, right? Every one of the projects that we work on, we make sure the business and the development team are together involved working through that whole process. It's no longer, you gave us a spec, we'll go away for eight years and come back and deliver something. And then you're gonna tell me it's wrong. I don't even remember asking you to develop it in the first place. But yeah, in the land of DevOps, there's this notion in the agile thing about two in a box. But the two is always two developers kind of work in hand and glove. But in reality, the two in the box should be one developer and one business person who are kind of building something together. I always like to visualize that. I mean, we talk a lot about DevOps at this conference. So you have the operations, developers, and we should merge these two worlds and make a cross-functional team. I think we should do the same thing if you look at application development and the business and kind of merge these two worlds as well. So have a cross-functional team that includes people from the business or the domain experts that can immediately express their needs into visual models. Business people, by the way, don't distinguish between developers and IT. They pretty much hate you both and they don't really care. Can you, we're at a Cloud Foundry thing. So can you take a minute and describe, you know, what is the connection between Mendex and Cloud Foundry and whether you care and why are you here? Basically, we care about Cloud Foundry a lot, but in two different ways. So first, if you look at Mendex as a platform, it's not just about application development. It's about supporting the entire application lifecycle. So from your first idea to building the application, to deploying it, to gathering feedback, and then so the whole cycle is supported in our platform. So for the deployment part, we have our own public Cloud and that's based on Cloud Foundry. So open source Cloud Foundry running on Amazon. So if you build an application, basically model your application, you can just one click upload that to our Cloud and then it will run on Cloud Foundry. And of course, that's something that's speeding up the whole process of our internal engineering. So instead of building that whole layer ourselves, we pick it back on top of Cloud Foundry. So that's one reason. The other way, the reason why we are really happy with Cloud Foundry is that it's a standardization of our deployment target. So we want to offer our customers flexibility and freedom in where they wanna run their application. So we really have a multi-Cloud strategy. And with Cloud Foundry, we can run everywhere. So we run on our own public Cloud. We can run on any Cloud Foundry vendor that's at this conference. And we can, so you can run your application on-premise, on a Cloud Foundry distribution, or on any public environment that's based on Cloud Foundry. And for us, that's of course a very easy thing to do because it's all the same API and it's the same layer. So that's a great thing.  There's a runtime somewhere. So I mean, generally it's invisible, at least to us. We use the Mendex Cloud to deploy our applications. And I think it's taken a lot of the burden off of us from just from a support and operation perspective, right? We can log in through the Mendex platform to kind of see all of the different metrics associated with our applications running in the Cloud, view the logs, do all the kind of normal support and operational stuff without having to kind of go down a level layer, a level lower. So we have 20 applications right now. Yeah, it's pretty invisible to us.  I'm not really checking to see if it's Teflon, but it's the Teflon that makes the pan non-stick. And I think that many of the business procurement environments aren't going to look that deep.  And I look at this and I was like, well, this is traditional shrink rack proprietary software on a physical server in somebody's garage. This is not really Cloud. I know we don't have to buy a server, but there's some differences here. So I think that translation, what Cloud Foundry gives Mendex and what I expect as a consumer of Mendex is the ability to dynamically scale. Because I have a lot of applications that have the cyclical peak. I'm looking for portability. I'm looking for continuous integration that provides no downtime to me. And I'm fine with multi-tenant environments, but because I'm in higher ed and I know our cycles are all the same, I want to know that that scale is partitioned and I want to know that another customer that does something bad cannot hit my data stream. So I think it gives Mendex and other companies capabilities that the business is looking for, but they're not defining it in terms of I'm looking for Cloud Foundry per se. All right, a lot of times the business doesn't even know what the order of the possible is. So they're kind of, since we're talking about that, I'll bring this up. So the buzzword of the day is digital business transformation, which means nothing. I mean, it's a lovely term, but it means absolutely nothing. But at the end of the day, are you seeing a change in the way that business people think about IT or engage with IT as a result of what you guys are doing? And can you describe a little bit about what you're seeing on that side of the house in terms of how they interact with you or even think about things? Sure, I mean, I think you definitely see a change. I think traditionally, they come to IT with an idea and kind of a specific set of things to do and we deliver it exactly to that spec, which is maybe not exactly what they need. The level of interaction that we have now in kind of using these low-code environments allows everyone to participate in the entire process. And we actually are much more in touch with our clients around the university, right? We talk to them four or five times a week, even though we have maybe one scheduled business meeting during the week. So I think that iteration has been very helpful and allow us to kind of switch gears depending on what they figure out as they use the system, right? Because it's that whole cycle of actually using a system, providing feedback and we make additional changes that has helped, which is not the traditional design process for IT, especially in universities. Yeah, absolutely. And I think in our business, we going back toward Mendex era, where we were like, how are we making this system work for us for this? And I was like, well, we just name where it says client, we just put the project ID and then we just put this over here in this field and then we put a little star in the corner and we're like, no. So now we're able to, and I think it's a double bunch to work with Mendex because I consider how quickly we can iterate the application, sometimes on some of our apps, multiple times a week, releasing a new version, but people start to see, oh wait, we can actually make it do what we need it to do and then it becomes a barrage. Do this, do that, do that, we need this, what about it, those, those, those, and then it's like, oh, slow down now. We gotta actually test this stuff and make sure it's reliable and that there's nothing in the buggy that's gonna take the whole thing down. That's been our experience, I think it's a deluge. Well, I've seen a huge change and this whole transformation in the technology industry is a chance for a do-over with your relationships and the dialogue that you're having with your business stakeholders. I still think that there's a long way to go. I think we see it most in mobile that in business environments, especially in very distributed environments, it's very difficult for people to change what they've done for 20 years and so you may be able to build something, a business process in a mobile, but it doesn't feel like a mobile experience and especially in higher ed where we have students and they're willing to say, why, why does it look this way? Why would I have to do it this way? Being successful really requires completely rethinking how you break down some of those traditional barriers. I mean, the different business units across the areas, how many steps, how can you simplify, how can you streamline? And that also often requires governance decisions, changes, changes in funding allocations, working with your CFO's office to go from CAPEX models to OPEX models so that transformation and change with the business isn't really just about what we're building, it's much more fundamental in terms of how your organization is leveraging technology to meet their needs and the willingness to go through that change process. It's always funny to see if people start to feel that application development can be faster, you see a change in the whole organizational behavior and that's, so basically what we see a lot is that if you build your first application and you do it in 30 days instead of a year, then people start to ask what Jonathan was saying, like, okay, can you do this and this and then it becomes the next challenge, basically. But the beauty is that people change their level of expectation and I think that has two effects. So one is that they start coming with new ideas again because in a lot of organizations, people don't share their ideas anymore because if you share your idea and people say, oh, great idea and then two years later, nothing happened, you stop doing that, you stop sharing your ideas because it doesn't help in any way. If you know that your IT organization or can build applications a lot faster now, then you start coming up with new ideas again. So the whole kind of experimentation and innovation within the company is changing a lot. So that's an important aspect. What's also important I think that is the change that if people understand and believe that you can quickly build an application but not only the first version, but you can keep on changing it, that will lead to smaller applications and we all want that, right? So it's a big monolith, difficult to maintain, difficult to change, but people will request basically a big application because they know if they are now on top of the list to be served by IT in developing the application, the next time you will be on top of the list is two years from now. So you better ask everything you wanna have and have that single application. So you need to change that behavior as well so that you can focus on smaller applications, iterating quickly and things like that. So I think that's also an important aspect of changing that whole behavior and how you work together in the organization. So business people play a lot of golf and so the one term they understand is mulligan, so Mendex is a giant IT mulligan. When you think about the term, you use the smaller things and one of the buzzwords of the day is microservices, nobody's really quite sure what the hell microservices is so I'm just gonna say it's something that's smaller than what was there before. So are we gonna like get to the point though where the thinking of microservices and agile is built into the product itself and it's not something I need to think about it because if I need to think about it it's probably not gonna work. So are we kinda getting to the point where the concepts are baked into the tools? I think in any case, you always need to think for yourself and decide if you build an application or build a service, so what the granularity is. And that doesn't differ for Mendex or Java or whatever language you use to build applications. So but that's the beauty of Cloud Foundry as well. It's a polyglot environment so you can build your microservice or small applications using Mendex. You can extend that using code by the way so it's not just only visual modeling, you can extend with Java or JavaScript so you're never stuck into the environment. You can also, in your whole landscape, deploy 10, 20 Mendex applications but also your Java microservices to extend the capabilities of what you're doing. So it's basically your choice to how you organize that whole landscape. So with that in mind, can you guys maybe describe how it is that the usage of Mendex or the applications you're building, are they more externally facing or are you seeing more integration externally with other entities and is that kinda changing the way you think about your role? I think it definitely integration is one of the top priorities associated with a lot of the applications that we use. In an educational environment, we have a student system, we have our ERP and then we have lots of departmental applications around the institute that we expose in whatever way we can and how do we pull that information into an application? Traditionally, it's always kind of extracting data and pumping it into another database which has its own issues. But I think with a platform like Mendex, we're able to create these kind of microservices that can interact with the data from these different systems. We can leverage existing APIs that we have in systems around campus and pull all of that information into Mendex. So we actually have applications that our students use that connect to the traditional student system to pull data so that they can change their major or drop classes, things like that. And then we also interact internally with departmental applications for patent invention submission, things like that. But Mendex acts as kind of the UI layer and a portion of the integration layer. We have a new instance, a client that's a very large music festival production company and they wanted a single source of truth for our schedule. And so our Mendex application that we developed for them is the single source of truth for what artists is going on what stage when for all of the music festivals that pushes out to their different WordPress sites for the public basing schedule. And it's basically the talent buyer enters that information and it flows through all of the other locations. So it's a single source of truth for that because it's hugely important for their business and to their consumers. It's just one example of really using Mendex and also our internal application, the single source of truth or job codes and how much is labor costing and all these types of different things. Yeah, I'll build on that. I mean, I think the big win for us is really moving away from these applications where shadow data that would never map or align to the data in the core ERPs was being collected in 50 different areas or really being seen as being lost in cracks. So you lose all that downstream ability for personalization, data analysis, actual data. So Mendex has been important in terms of being able to support the variation in the app needs but using our tool of MuleSoft to come back and connect to the data. I also really think that down the road where there's amazing opportunity is that if we can care less about what the app is because centrally we care about the data and if we look at what's going on in the mobile environment where apps are disposable, they work for a function, they're a micro function, they mash up data across the way. When things change, somebody grabs a new app and they're mixing and matching. So if our organizations could deliver secured data and these thin lightweight development platforms we can move to that area where we've got disposable apps. Yohan, can you kind of give us a little history of where we are and I'm gonna ask this question because I admire developers as much as anybody but truth in the matter is they're kind of snobs, right? And basically they're like, I got my programming language and my skills and this is the best thing ever and this is all that is in the world. But so back in the day, we had everything from 4GLs to modeling tools and we're kind of down a similar concept and path but connect the dots a little bit about how Mendex came from that thought process and how you kind of got to where you are now. So basically if you look at the history of programming we always have been focused on, I'll use the words again, abstraction and automation because we always wanted to go to a higher abstraction level make sure we could be more productive from assembler to second and third generation languages and of course the whole idea of low code or whatever you call it, it's not new. We have been trying to speed up development for a long time and so you look at case tools, 4GL tools and maybe you have bad memories with that that could definitely be the case and I think why a lot of these tools have failed in the past like 4GL tools. A main reason there is that they basically missed the paradigm shift in the market. So they were focused on building, for example desktop applications very quickly but then the web game or mobile game. So basically at Mendex from the beginning we have been really, really focused on making sure that we are not speeding up how you built yesterday's applications faster but always focused on how to build tomorrow's applications faster. So making sure that people can actually innovate because that's the part of the organization that has a need for speed. The part of the organization that wants to innovate, introduce new services, new business models, you wanna quickly experiment because innovation is all about experimentation. So you need to try out 10 new app IDs in one month and then throw nine away and maybe that last one is the one that will really change your business. So you need to kind of feel fast and keep iterating and if you look at applications in general, so if you wanna build tomorrow's application, I think the next paradigm shift in applications is what I would call smart applications. And if you look at a smart app, that's for me that has three main characteristics. So a smart application is context aware. So it knows where the user is, what the user is doing, the context of the business process, maybe sensors around the user with the internet of things. So that's one characteristic. The second one is that a smart app is intelligent. So it knows how to process all that data and make decisions using algorithms or machine learning. And if you combine these two things, being context aware and being intelligent, you come to the third characteristic of a smart app which is proactive. So making sure that instead of the user going to the app to trigger an action or to get information that the app comes to the user using interactive push notifications or whatever technologies we can use. And then, so I think we have to focus as application developers, as platform builders or making sure that we enable the next generation of applications so that we actually help our customers to innovate quickly and to really enable them. So it's not just about how you build applications or how you deploy them or how you operate them but also the type of applications that you build. Falling fast is also one of those cute little Silicon Valley terms that business people don't get either. They're like, not something they really wanna do. They like phrases like winning sooner. Can you guys describe what do you know now about implementing Mendex or moving to this model that you wish you knew before you started? Yeah, I mean, so one of the things I think we mentioned before around once you have that paradigm shift and the users realize that their ideas can actually be implemented in a short turnaround time, managing kind of that flow, right? Cause I think the trap that you can get into is that there's a lot of churn as well, right? That you're kind of starting and stopping and re-implementing the same idea, maybe like two or three times during the course of the project which sometimes it's okay, but I do think there's components of traditional software development that still apply and how you think about architecting an application and so how does that translate into kind of a low code environment like Mendex? And again, I think you get to it. I wish we had gotten to it faster. It probably took us maybe six months too long to kind of figure that out. But now that we have, I think we're producing more efficiently and at a faster rate. So that question a little bit differently and I do agree with what you said. From our standpoint, using non-coders to that are actually the developers now, I just wish that they have the experience the last year and a half so that I can work on the first application which is our internal ERP, which is the worst one. It's great, but the user interface and some of the way that the microclosers are written in the background, now that we've done 15, 20 since then, wanna go back and say, okay, let's redo this now that we've learned all this information and learned all these skills inside of Mendex. So that's a little bit of a different answer, but that's what I would say, at least for us, because we're using non-developers. Yeah, I would echo that too. I mean, and one of the things in any environment when you introduce a tool where it can be faster and easier, the first natural reaction that everybody has is put as much energy possibly can. I used to teach photography, same thing, digital. I mean, they had enough things in there where this would never load over time. So learning restraint. And we saw the same process where, oh, this is great, we can add all of these things in. But then as the business users then went through the process, they're like, oh, I'm scrolling through 18 screens. Let's simplify, let's come back. And I think that I wouldn't want to eradicate that. I think the group learning process in and of itself was valuable, but to factor that time in just, especially when a particular team comes together for the first time. So I said earlier, I go to conferences all the time and I talk to people at the shows. And basically, everybody's there for the same reason, right? They wanna steal a good idea from somebody. So what's the best thing that you guys have built in Mendex that you think is the coolest thing you've seen so far? And then what's on your agenda over the horizon? Sure. I don't know. I'd say we have probably two separate applications currently that I would think is a good idea. We have a nanotechnology laboratory on MIT's campus. And so a lot of the tool scheduling and tool management around laboratory equipment is something that's very time consuming and just takes up a lot of cycles. So we have an application that helps us with that, not only to schedule the tools, but actually start up jobs. So we're using Mendex in conjunction with like Bluetooth and other technologies to actually interact with hardware equipment within the laboratory. And that's been a huge boom to the nanotechnology laboratory because they have hundreds of research projects that are using equipment on a weekly basis and that interaction needs to be automated as much as possible. So that's kind of one thing that I think is pretty neat, allowing us to interact with hardware equipment. The other is MIT is a research university, so they submit thousands of patent applications every year. And it was all paper, all paper. So automating that, digitizing all of that information and kind of streamlining that whole process, I think they've been able to process patent applications and half the time that they used to before and they're constantly kind of just building upon that. So our legal group at MIT is very grateful for kind of this iterative cycle for application development. And we've been working with them for a year and a half just constantly either adding new components or adding new applications to that entire environment.  Yes, no more paper and lawyers like paper, so. Yeah, we've been doing a lot of Mendex in the area. Events, interaction with the Mendex and events and music festivals, trying to blend the production of the event with the user's experience where we're doing the RFID years spans and we're having RFID years out in the field and in the venue and we've left the user and linked their social accounts to their wristband and linked their credit card for spending money at the various concessions and whatnot. And that's all great money from the money standpoint, but for the experience, when someone walks into the room or have social media visualization between the artists on a huge stage and large screens, we'll actually read their wristbands that are in that area and just use their social content on the screens. Obviously stuff that they would give us approval to use, but really, we did the sporting event where there was, you go into the convention center and you can play the punt pass kick thing and get a prize or something. And well, when they walk up to that and scan their wristband and you look up what their favorite team was and some other information about their favorite number on the screen in front of a mother playing the game is their name on the back of the jersey, their favorite team. So really kind of augmenting the life of that experience. So full disclosure though, you were working on something on your way here, right? So the upside of this thing or the downside, depending on your point of view, is that you can work on it anytime, anywhere. Yeah, actually, I was a lot on the outside. Actually, the internet connection on the plane allowed me to commit it and restart the actual as usual that we were able to so quickly meet their needs. Isn't the internet on the plane great? Yes. Okay, so you usually don't use regulatory compliance and cool in the same sentence. However, I will say that using Mendex in that area that is so close to our core mission in furthering science and for our economic needs, I mean, Grant is one of the revenue streams into our organization. It's been extremely successful, but I'll answer the question a little bit differently in terms of where I think there's additional need and potential for products like Mendex and especially if low-code can get even further down to no-code environments down the road. At Georgetown, we participate in a number of research activities and partnerships with our vendors like Verizon and municipalities, public sector, DC government, looking at smart cities. So how can transport, mobility, wireless, internet of things, sensors and data come together to improve safety, sustainability and your life in general through public private and higher ed partnerships? What's interesting to me is that when you're, if any of you have ever engaged to build applications for smart cities environment, something like over 90% of those implementations are in the coastal cities only. And when we talk about the digital divide, which we've been talking about for 30 years, we're seeing that accelerate with the pace of change and what's happening in the middle of the country, you're having large areas that are not being exposed to this level of innovation and automation because you don't have the IT areas in those areas that's all moving to the coast and the urban environments. But a product like Mendex or Cloud Foundry that can broaden that accessibility or the democratization that you talked about, there are enormous applications in those areas. So I think that that's really something in terms of looking at not only the sectors that are jumping on quickly, but what are the untapped markets that are out there as well? So we have one to-do list item for Johan so far. So what do you guys add? What do you want Mendex to do next for you? Well, so I think he already talked about kind of smart applications, right? So the ability to use smart services within applications to kind of, again, I think the biggest thing is being proactive with users of applications, right? More advanced notification of activities that people need to perform is a huge boom. I think the other is around data accessibility, right? So we talked about data integration between applications, but one of the things that I'm always, you know, keen on is data accessibility. So there's data that a Mendex application can generate. So how do non-Mendex kind of consumers use that information as well? And that's always a big boom. So I want the integration to be more bi-directional. Yeah, I would agree with that. I mean, we do a lot of interesting things. We talk about RFID, there's fans early on in RFID in general, we've done a lot with that, both on the consumer-facing side and in our warehouse, so we just keep all of our equipment, lighting sound, video equipment for live events. But we were able to actually write a widget to inside of Mendex to be able to read RFID with an Android, a regular Android device. And a lot of our competitors and even competing software products that we may have bought prior for inventory tracking, they wanted you to buy this $1,500 RFID reader handheld thing. We're like, okay, well, Mendex plug-in that we created runs on those Android devices that have an NFC reader built-in, so that's $99 for a prepaid device. So they become disposable at that point. So that's huge for it. Nothing comes to mind other than that, but we were able to make it work, even though it wasn't built-in functionality just yet. And more of those kind of things, like you said, how the sensors access to physical data. Well, that's the beauty, of course, of not closing the entire environment but making it open, that you can always extend using JavaScript or Java. So basically, you're never stuck and you can keep on building additional things, although it's not out of the box in the platform. So I just want to check, do you have a date for the deliverables on any of this? Tomorrow. You all need to commit that. It's low code, you should be able to deliver. All right, I want to thank our panelists for sharing their knowledge and their insights. Guys, you guys were great. Thank you all for listening. Have a great show. Thank you. Thank you.


accountability. Make sure everyone on your team knows holding someone accountable is something you do for them.   Holding someone accountable is the best gift that you can give but before you can hold someone accountable you have to be crystal clear on what you're holding them accountable to and most importantly the level of expectation at which you're holding them to.


So what I would like to do in this talk is to discuss to what extent we can understand quantum phase transitions within the non-perturbative or exact or functional logic. So the outline of the talk will be something like this. I will start with a general introduction to quantum phase transitions. This should be understandable by anybody here in the audience, even if you're not in the field. And then I will turn to the thermodynamics near quantum critical point. And in the last part of the talk I will discuss more recent work about response functions, so correlation functions, namely the scalar susceptibility and the conductivity. So let me start with the quantum phase transitions. So quantum phase transitions are transitions which occur at zero temperature. So they are not driven by thermal fluctuations, but by quantum fluctuations and they are induced by a change in a parameter, typically in a parameter of the Hamiltonian. So experimentally this could be an external field or this could be the chemical composition in a solid and so on. So here I give you two simple examples on quantum phase transitions. The first one is the transverse field ising model. So you're probably familiar with the Hamiltonian. So you have the ising model plus a field H which is in the x direction. And when you change the ratio J over H you go from one grand state to another one. Namely you go from a prime magnetic grand state with all spins aligned in the x direction, so along the field, to a grand state which is doubly degenerate, which is ferromagnetic. And it has all spins along the z direction pointing either up or down. So this is the first example. The second example is based on the Borser-Bart model. So the Borser-Bart model is a model of bosons hopping in a lattice. There are three parameters in this model, T which is the hopping amplitude for a tunneling between sides. Mu is a chemical potential so that the mean density of bosons is fixed on average and nu is a local onsite interaction. This is the phase diagram of this model in the plane T over U and nu over U. So of course this T over U is large, the kinetic energy dominates, and then you expect Bose-Einstein condensation, so a super free grand state. This is what you see here. If T over U is small, then you see that each time the densities commensurate, for instance if you have one boson per site, you will have an insulator with one boson per site on average. This is known as a mod insulator. So what you get here is a series of mod insulating phase with different densities. So these are lattice models, but of course if you're interested in the low energy physics, and in particular in the universal physics, you can start from a quantum field theory. For instance for the transverse field ising model, this will be just the quantum 01 model, which is nothing else but a quantum 54 theory with ising symmetry. And for the Bose-Einstein model, there are actually two universality classes depending on whether the transition occurs at fixed density of bosons or not. So basically you can induce the transition either by varying the density or by varying the strength of the local interactions. And this gives rise to those two different universality class, which are the universality class of the Bose-Gas orbit of the quantum O2 model. So in the rest of the talk, I will discuss the quantum 01 model. It's a very common model in condensed matter physics. So it describes for instance cold atoms in optical lattice when n equal to, this is for both of our model I just discussed. It also describes quantum ontiform magnets when n is equal to 3. And there are many other examples. The action is quite simple. It's just the action of the phi for a theory. So here phi is an n component field. What's special here is that the field depends not only on space but also on imaginary time. This is where quantum mechanic comes in. And the only place where you have a temperature dependence is here in the integration over the imaginary time. In particular, air not here and U not are supposed to be non-thermal parameters. They do not depend on temperature because they just come from the Hamiltonian of your system. So of course you see that at zero temperature, you just recover the classical D plus 1 dimensional 01 model. We know that this model has a phase transition which is governed by the D plus 1 dimensional Wilson Fisher fixed point. So you have a diverging correlation length with exponent U. You have a diverging scale in time as well with some exponent Z times U. And of course here for obvious reason you have a Lorentz invariant action Z is equal to 1. So now what happens at finite t? So you see that the only effect of a finite t is here. So it's as if you would put your system in a box of finite size in one direction, the direction of imaginary time. And therefore you can just very simply understand the phase diagram by comparing the size of a box which is beta 1 over temperature with the correlation length in that direction at zero temperature. And this is why you get two crossover lines here. And this gives rise to three regimes that I will discuss very briefly. The first one is the quantum disordered regime. So here at zero t you have already a disordered system because of quantum fluctuations. Therefore if you add temperature on top of this it doesn't really matter and the correlation length is essentially temperature independence. The second regime is the regime here in the middle. It's called the quantum critical regime. So to get here you tune the system to the critical point and then you add temperature. So you start from a scale invariant system at finite t. Therefore there is only one energy scale which is the temperature and everything has to scale as a power law of the temperature. And in particular the correlation length will scale as one over t because that is equal to one. And finally this is this regime here the renormalized classical regime. So at finite at zero t there is long range order and so if you go to finite t you will have a disordered system because of thermal fluctuations. So here I forgot to say that I was considering a two-dimensional system and I assume that n is equal to three at least. Although it doesn't really matter. So in this regime you have just the usual physics of thermal fluctuations destroying long range order in agreement with Mermin-Legner theorem. Okay so what do we want to calculate? What do we want to understand? Of course the first thing is to understand what's it's happening at zero temperature but in the end what we really want to understand are the properties at finite temperature because in any real experiment you have a finite t. So what we do want to understand is how does the presence of a quantum critical point modify the physical properties at finite temperature. Properties that might be experimentally accessible. So one thing for instance we would like to calculate is the thermodynamics. Another thing is time dependent correlation function and of course you want this in real time so this is a real issue as you will see. In each case if you are close enough to the quantum critical point there will be scaling forms and if you have scaling form it means that essentially what you want to calculate is a scaling function fn for the pressure here. So universal scaling function which depends on n the number of components of the other parameter and another scaling function here. Well so now how do we address all these questions within the non-pertributive functional Rg? So we do what we are used to do as theorists. So we had to reaction an infrared regulator term which is essentially a mass term for the fluctuations. It is this mass is q and omega a dependent. The typical shape of this cutoff function is shown here. So if you look at momenta or frequencies larger than k essentially you do nothing. This r function is very small but at low energy if q or omega is smaller than k then you have a large which is pretty large and therefore it freezes out the fluctuations at low energy. So now the partition function is k dependent. You define as usual the scale dependent effective action which is the Legendre transform of log of z the free energy to which you add for you subtract for convenience this term but this is not very important. So we know that there is an exact equation for this function and if the initial value of k which I call lambda is large enough then you can assume that all fluctuations are frozen in the system and therefore you start from mean field theory which is defined in this context by gamma lambda being equal to the to the action. So I remind you that from the scale dependent effective action which is essentially the Gibbs free energy of the system you get not only the thermodynamics but also the correlation functions because this object is a generating functional of one particle irreducible vertices. Okay so how do we proceed to compute the thermodynamics because this will be my first point. It's not possible of course to solve exactly the exact rg equation so one possibility is to use a derivative expansion. Essentially this is an and that's which is based on the derivative expansion of the effective action. So here I show you the most general and that's the second order in a ingredient. So there are various terms there is one term which is poorly locally without derivative this is the effective potential and from the effective potential you get the pressure very easily. So you do this of course numerically because you have to solve coupled equations for the z function the y functions and the effective potential u. Once you know this effective potential numerically you can check the scaling form of a pressure and if it's satisfied you also get this universal scaling function fn. So here I should say maybe in more detail what I call delta so delta is a characteristic zero temperature energy scale so typically if you're on the disordered side you will take delta to be the excitation gap of a system at zero temperature. If you're on the ordered side then you look at the symmetric point symmetrically located with respect to the quantum critical point and you take the excitation gap at this point okay. So this is the calculation we did a few years ago with Adam Van Son and others and this is what we got for the scaling function as a function of n. So for large n it is exact we get the exact result so it's something which is non-monotonous and for small n we get something which is non-monotonous for instance the red curve here when n is equal to 2 we can do the same jump for the entropy of course and any thermodynamic function. So when we did this calculation we thought it was the only calculation for this and in some sense it was but then Adam Van Son with somewhere in the room realized that some people have done the same calculation but in a completely different context so what Adam realized is that if you use the usual quantum classical mapping which is from here then you go from this 2d quantum system at finite t to a three-dimensional system in with a finite thickness here and in this kind of situation you have a casimir effect and so I'm not going to tell you more about this because Adam's talk will be on this so he will give you much more detail but this casimir effect so this casimir force has a scaling form and it turns out that the scaling function of this casimir force which is poorly classical here is essentially the scaling function which appears in the equation of state of the quantum system so therefore we have a way to compare what we got in the quantum setting to what people have computed using essentially three-dimensional Monte Carlo simulations of these models and here you see what the comparison gives so this is for n equal one and equal to n equal three the continuous line is what we calculated with a non perturbative or g and the symbols are data from various Monte Carlo simulations so you see that the comparison works very well and so again it works very well so I should mention also that the non perturbative or g is the to the best of my knowledge is the only or g approach which allows us to get this universal scaling function I'm not going to tell you more about this because Adam will tell you more okay so now I turn to the last part of the talk correlation functions so examples would be for instance the order parameter susceptibility which is just the two point functions another example is the scalar or x susceptibility which is the correlation function of the square of the other parameter I will tell you why we we consider this object and the last example would be the conductivity which is essentially a current current correlation functions so now it turns out that computing correlation functions is much more difficult than computing thermodynamics for three reasons actually mainly two the first reason is that we are looking at the strongly interacting theory because we are interested in the vicinity of the quantum critical point actually this is not too strong a problem because we have Rg and Rg does the job quite well the first real problem is that we are usually interested in the full frequency or full momentum dependence of the correlation functions and therefore the derivative expansion is not sufficient we have to do something better and the last problem which is a problem common to many fields not only Rg actually is that we have to perform an analytical continuation from imaginary frequencies to real frequencies so I will discuss all these problems so first a yeah I will discuss only the scalar susceptibility and the conductivity I will not discuss the other parameter susceptibility so why are we interested in the scalar susceptibility so first let me briefly remind you about the difference between the longitudinal and the scalar susceptibility if you look at the dynamics of this model in a mean field picture so since you have this Mexican hat shape potential which is shown here you expect that there will be a gap less fluctuations corresponding to the Gaussian modes of the system and you also expect that there will be a gap excitation corresponding to the amplitude mode of the system and indeed if you do a mean field calculation this is what you find you find n minus 1 Gaussian bosons and you find a longitudinal propagator which has a gap now it turns out that this result the gap in the longitudinal correlation is not correct if you go beyond mean field you will see that there is coupling between longitudinal and transverse fluctuations and in particular a longitudinal fluctuations can emit two Gaussian bosons and the outcome of this is that the longitudinal propagator is completely dominated by Gaussian bosons and for these reasons it is not gap that is diverges like that in the infrared limit with this exponent 3 minus t divided by 2 so for a while people were quite pessimistic they thought there is no way to observe this to take the amplitude situations because the longitudinal propagator is dominated by goldstone mode so this is what we see but then there was a very interesting paper by but then there was a very interesting paper by Podolski and coworkers telling us that maybe we were too pessimistic too pessimistic here if we look at the different susceptibility namely the scalar susceptibility which is the correlation function of phi square we might see the amplitude the amplitude mode as a well-defined excitation so this amplitude mode is also called the x mode because there is some analogy between condensed matter here that is called physics and high energy physics even though the analogy is not perfect however nowadays it's the terminology which is used so I will also use this terminology so what we want to do is to compute this spectral function it has a scaling form here and we want to compute this universal scaling function so how to compute the scalar susceptibility so we need to compute the frequency dependence of the correlation function the derivative expansion is not sufficient but there is a nice method which was proposed by a Blaiseau Mendez and Schreborgs two of them are in the room I'm not going to tell you in detail what it is about but essentially the alley is the following if you want to compute the propagator for instance you need an equation for gamma 2 which is the inverse propagator but these couples to gamma 3 and gamma 4 and in turn gamma 3 will couples in your j equations to gamma 4 and gamma 5 and so on so you get an infinite hierarchy of equations that you have to close in some way if you want to solve this hierarchy of equations and what BMW noticed is that if you consider gamma 3 and if you put some external momenta to 0 then it's simply related to gamma 2 just by a field derivative and this allowed them to close the equations so of course you have to justify why you are allowed to put some momenta to 0 to set some momenta to 0 in the RG equations but they did this very nicely so I refer to to their work so we apply this method to compute the scalar susceptibility so the scalar susceptibility it's a four point correlation function so we cannot exactly directly apply the BMW scheme apparently so what we have to do is that we have to introduce a bilinear source h which couples to phi square and therefore the effective action will be a function of h now and then you can relate the scalar susceptibility to the vertices of the theories and now the vertices of the theories they are kind of generalized vertices because you can derive the effective action with respect to the field or with respect to the external bilinear source so therefore you have vertices gamma and n and you end up with this expression and it turns out that you can use the BMW approximation to compute these vertices it works very well and you get closed equations with the momentum dependence so now last problem analytical continuation the problem is the following what we compute is the correlation function on the imaginary axis for that is for Matsubara frequencies that what we want is the retarded part of the correlation functions that is the correlation function for real frequency and this is done formally at least by some analytical continuation from this axis to the real axis so there is a relation which is written here between chi of i omega n and the spectral function that we would like to calculate the problem is that it's very hard to invert this relation so what we do in practice we use a Pade approximate method it's a method which works very well at zero temperature and if you have data without numerical noise which is our case here so the idea is quite simple you compute numerically chi of z for a set of Matsubara frequencies and then you assume a continuous fraction expression for your correlation function so you have what you have to determine is a one up to a m so you do this easily because you know chi of z at the end point so you can rewrite this as a Pade approximate and once you have this continuous fraction expression you can easily do explicitly the analytical continuation and so I said it worked quite well there is a recent application of this method by colleagues in Paris which shows that it really works very well okay so what did we get so here I showed what we got for the spectral function of a scalar susceptibility from n power g and we compare with Monte Carlo simulation so this is n equal to to be compared here so the scale is not the same but if you rescale everything properly you will see that these two scaling functions are very close together so we have done the calculation for any values of n and the outcome is that there is a well defined in this model x resonance for n equal to you see it here also for n equal to 3 but for higher values of m the resonance is not there the coupling to Boston boson is too strong for the resonance to survive okay so one thing which is interesting is to compute what would be the Higgs mass is in high energy physics which is the position of the resonance if you scale this position by delta the zero temperature energy scale you get something which is universal and here I showed you what people have obtained in the last years so the mean field result is square root of two so it's very far away of what we finally get here I showed you our recent result using n pair g bmw I think this is the best we can do with this non perturbative Rg because bmw is quite an elaborate approximation scheme and then I show various results obtained by various groups either with Monte Carlo simulations or quantum Monte Carlo simulations or exact diagonalizations and these results were also obtained from various models one model one model on a lattice both about model for n equal to x y model for n equal to or isambert model for n equal 3 and as you see I think that there is a nice convergence of the most recent works and you see once again that the n pair g is quite a reliable here nice agreement between all these results okay so now I turn to the last part of the talk which is the calculation of the other conductivity so it's a very challenging problem in this field in condense matter understanding the conductivity in the vicinity of the quantum critical point so essentially there are two methods which have been used so far to compute the conductivity the first one is quantum Monte Carlo simulation so quantum Monte Carlo is a very powerful method to compute things in imaginary time in imaginary frequencies but then the problem is to do this analytical continuation and in particular I don't think there is any reliable method to do this when the frequency is smaller than the temperature and in particular as you know in quantum Monte Carlo simulations you have statistical noise on the data so it makes things even more difficult the second method is based on holographic models which come from this ADS CFT correspondence so it's hard to say whether this will teach us a lot but I think the main problem is that the relationship between these models and the models we are interested in condense matter is not always very clear so some people are now trying to mix both approaches so recently with Felix Hose we'll talk this afternoon we have tried to use the non perturbative RG to understand the conductivity and this is what I would like to discuss now in a few words Felix has a talk this afternoon and he will give you much more technical detail so essentially we start from the action of the ON model and we make the ON rotation this global invariance a local one by adding a gauge field so it's a non-abelian gauge field so you can write it like this with TA the generators of the S1 group we also use it's very important in this approach a regulator which is a gauge invariant by using a covariant derivative here and then we can define the current in the usual way and compute the conductivity which is essentially the current current correlation function so unfortunately it was not possible to use the BMW approximation because it turns out that the BMW approximation does not satisfy the gauge invariance of a theory and we were not able to go around this problem so what we did is that we use the derivative expansion which means that so far we are able only to compute the low energy limit of the conductivity so this is the most general expression of the effective action to second order in derivatives so you see that compared to the previous case where I discussed thermodynamics there are two new terms so you have two new functions x1 and x2 f here is the field strength and these x1 and x2 functions essentially determines the conductivity so this is what we have to calculate so here I would like to stress that this gauge field is not a dynamical gauge field it's an external field we use it just to compute the response function however we include it in the action from scratch because it helps us to satisfy gauge invariance so it's a crucial point in in this approach so what would we like to calculate so the real challenge in this field is to compute the finite t connectivity in this regime why because as I said before so here you are at the critical point and at finite temperature so there are no no well defined quasi particles in the system so it's not possible to use any type of Boltzmann like approaches equation of a transport equation so you really have to do many body calculations but this is a very long time goal I don't think we really understand what's going on here with any method so here I tell you what we do understand so this is mostly the zero temperature limit we do understand that on the disorder side the system is insulating so it behaves like a capacitance see here on this side the system is ordered and the response is essentially a super free like response so it behaves like a perfect inductance and in between exactly at the quantum critical point the conductivity is universal which means that if you measure the conductivity in units of quantum and conductivity q square over h you get a universal number so that's a no prediction by fischer and collaborators so what we have computed with felix or mostly two things so far we have computed this universal ratio sorry I forgot to say that this ratio c divided by l so the c which is here divided by the inductance air is also universal if you look at two points which are symmetrically located with respect to the quantum critical point so we have computed this so it's shown here so here and here so the ratio is shown here so in the large end limit we got we recover the exact result which is known in the larger limit and it turns out that for n equal to there is a Monte Carlo simulations by this group gazette and coworkers and we get something which is in a reasonable agreement with this so another point I would like to discuss is the conductivity in the ordered phase so if you look at the ordered phase with n equal to 3 or higher there are two independent elements in the conductivity tensor which we call sigma a and sigma b so basically depends on whether the generator ta rotate the other parameter or not so we distinguish between sigma a and sigma b sigma a is the object that has been looked at most of the time so far and it's it's only the only component which remains when n is equal to 2 so typically it's as I said before it's essentially a super free like response and we have looked at sigma b which has been mostly overlooked and we found something which is quite interesting so first of all it's universal it goes to a finite number but what is very interesting is that this universal number is kind of super universal in the sense that it does not depend on n and this comes from the fixed point structure of this function x1 and x2 in the ordered phase so x2 actually goes to zero so it's not very interesting so here you see x1 at the fixed point of the ordered phase in dimensionless unit of course it varies with n but if you look at the minimum of the effective potential which corresponds to the physical case the case you want to study eventually then all the curves cross take the same value which means that this sigma b is a independent of n so there is a kind of a super universality here okay so I will conclude that how much time you have now I'm right so I have time to conclude it's time for question okay so the conclusion so I do think that the non-perturbative algae is a promising tool to study quantum critical point I think that the finite temperature thermodynamics near quantum critical point at least with this kind of relativistic symmetry is fully understood because we know the scaling the universal scaling function so we know the pressure we know the the entropy we know the specific heat so in principle we are able to explain or to compare with any experiments computing correlation function is much more difficult so for the heat susceptibility I think we were quite successful at zero temperature but still there is this problem to do finite temperature calculations and to do this we have to get away to find a way to do this analytical continuation so the paddy approximate method or the maximum entropy method cannot be used I think but there might be ways using the algae to circumvent this difficulty so here I cite a work by uh triple strodof and co-workers where they propose to do simplified calculations with simplified propagators so that we can explicitly do the matzubarassons and therefore perform explicitly the analytical continuation uh yeah so regarding the the dynamic me call conductivity it's a very challenging problem but there are very few methods that can address this I said before that quantum Monte Carlo simulations or the ADS CFT approach have their own problems so I think it's worth looking at this problem in more detail I will stop here and I thank you for your attention


to the lecture number 25 of the course quantum mechanics and molecular spectroscopy. Towards the end of the last lecture, we were looking at the transition moment integral which is nothing but f mu dot E. Now, we wrote that mu as an expansion of mu naught plus alpha plus beta R square plus gamma R cube plus etcetera, ok. This is the Taylor series expansion, the equilibrium geometry.  These are the coefficients, ok. So, this is nothing but dipole moment derivative. We will come to the usage of dipole moment derivative later in this course, ok. Now, in this case what mu naught is called permanent dipole moment which is nothing but the dipole moment at the, when the molecule is at equilibrium geometry. So, simply for example, if you have a molecule AB because of the electronegativity difference if there is a partial charge q plus on A and q minus on B and this distance is R, ok, R naught that is the equilibrium geometry then your mu naught will be nothing but equal to q times R naught, ok. Now, one of the interesting thing that you will still need in the time to evaluate in your transient moment integral when you write TMI this is equal to f epsilon i is the fact that we still have to evaluate the initial final wave functions. In the beginning of the course or throughout the course up till now what we have assumed that we have a Hamiltonian H naught we still do not know what that will exactly is into i is equal to E i i and H naught f is equal to. So, this we have assumed all while but we still do not know what is H naught precisely, ok. Now, what I am going to do is that let us start with a very simple problem that is the rotational state. So, what we look at is pure rotational spectra we look at the pure rotational spectra, ok. Now, if you have proved if you take the total if you have a molecule AB then and this is your R naught, ok.  Now, one can write such a Hamiltonian H, ok. Now, this is what this is the kinetic energy of atom A and this is nothing but kinetic energy of atom B and this is the relative or bond energy of AB, ok. So, we will go back to this same thing.   If U R ELB will consist of, ok, we will consist of three terms. One is electron nucleus attraction consist of electron repulsion nuclear nuclear repulsion and four electron kinetic, ok. Now, I want to make one approximation called rigid rotor approximation. Now, what am I saying when I say rigid rotor approximation? Very simply it means the following. If I have A and B and they are separated by distance r naught, ok, the distance is fixed. That means the nuclei are no longer moving, ok. So, the nuclei are at the fixed positions in this case. When you have such a scenario, then the sum of U R EL AB will be equal to constant, ok. So, the nuclei are fixed and in the AB molecule the nuclei are fixed, they are not vibrating, they are not ending, they are just fixed, ok. When such are fixed, when the two nuclei are fixed, then, ok, electron nucleus attraction will not change, the electron electron repulsion will not change, a nuclear nuclear repulsion will not change, electron kinetic energy will not change. All of them will be some constant numbers and when you add all the four constant, you will get one more constant. So, your U relative AB will be equal to constant. So, which means your Hamiltonian H R R, ok. R R because rigid rotor, ok. I will call rigid rotor as R R, ok. H R R will be now be equal to minus H bar square by 2 m A del square A minus H bar square by 2 m B del square B, ok, plus some constant. Now, one of the interesting things of, you know, energy measurements is that you can always measure energy with respect to some value, ok. For example, in thermodynamics, we only measure delta G's and we that is delta G's because we always think that the delta G of the standard states is 0, ok. And we measure delta G with respect to a standard state. Similarly, in this case one can think that one can measure energy with respect to this constant which is nothing but U relative AB. So, since I am measuring with respect to this constant, I can ignore it. So, my H R R will be now be equal to minus H bar square by 2 m A del square A minus H bar square by 2 m B del square B. There was a constant which I ignored because I am measuring energy with respect to that. So, this is my Hamiltonian. If you think of it, so what I have is the following, ok. I have A and A is connected to B by a stick that has length R naught and that is not changing. That is what I said rigid route, ok. So, the positions of A and B are fixed. Let us for a time being think that what all the possible motions that this particular system can undergo. Now, I told you the R naught is fixed, ok. So, when the R naught is fixed, it can only undergo two motions. One is that if you have A and B, ok, attached by R naught, then of course, it can move along x, say z direction, it can move along x direction and it can move along the y direction. So, the whole molecule can move in some direction, ok, along x, y and z, ok. Now, what else can you do? Because it A and B cannot move, then what can you do? It can rotate. A can rotate, A B can rotate like that without changing the distance or it can rotate like this, ok. So, A B can rotate and A B can translate. So, only possible motions are translation. What did I say? So, we had this Hamiltonian, which was nothing but h, ok, is equal to minus h bar square by 2 m A del square A minus h bar square by 2 m B del square del square B, ok. Now, we had two of them. So, there are two possible motions. One is the motion of A atom and other is motion of atom B. However, they are strict to each other. So, A and B cannot move independently. So, in that scenario, this equation can be written as minus h bar square by 2 capital M del square C m plus minus h bar square by 2 mu del square, ok. So, this is something called, it is called co-ordination transformation. I will not go to the mathematics of it, but I will tell you what. So, if I have a quantity A and B, ok, that is my body A and B. So, what you have is the coordinates of A and B, ok. And there is a mass of it which is m A and the mass of this is m B and there are some coordinates, ok. And what I will call it as R A and R B. So, the center of mass will lie somewhere here. So, this is center of mass, ok. So, this will have R center of mass. Similarly, you have another distance called this as naught which is nothing but R internal. So, there are two things that one can do is that instead having R A and R B, there are the vectors from the origin going to A and B. There are two vectors that are representing this system R A and R B. Instead of that, you could choose an alternate description that is R center of mass and R naught. So, it is an equivalent description of it. And one can do the math, ok. There is something called center of mass transformation. One can look it up in some textbooks, ok. And it will clean from that one can make such a transformation in the Hamiltonian without making any approximations. But there is only one thing that I need to define where your m capital M is equal to m A less m B, ok.  And this is called reduced mass. And this is total mass. These two Hamiltonians, so h R R equals to minus h bar square by 2 m A del square A minus h bar square by 2 m B del square B is identically equal to minus h bar square by 2 m del square center of mass minus h bar square by 2 mu del square, ok. So, instead of having two coordinates representing A and B, now you have an alternate description in terms of coordinates representing center of mass and internal motion, ok. And this is of course valid only when these are valid under this. Now, there are two things that let us go to the Hamiltonian equals to minus h bar square by 2 m del square center of mass minus h bar square by 2 mu del square, ok. Now, there is one issue is the following. Is that this quantity is the center of mass motion. That means the whole object is moving, ok. So, this is nothing but your, so you can now calculate your h R R as two quantities. One is h center of mass, less another is h internal, where h center of mass is equal to minus h bar square by 2 m del square center of mass. So, this represents the motion of the of the entire AB molecule. Now, this is just a motion of the entire AB molecule in some direction x, y, z or a linear combination thereof. So, this essentially leads to what and there is no potential acting on it. So, essentially this leads to what is known as free particle, ok. And we know the free particle is unquantized, ok. So, one can think of having a this one as h cm chi cm is equal to, ok, E cm chi cm where E is equal to h bar h bar square k square by 2 m is the minus h bar square k square by 2 m that is the kinetic energy. So, we do not have to worry about the Hamiltonian corresponding to center of mass, because that just represents the motion of the entire AB, ok. And just if you want to move a particle or a mass or AB molecule, ok. Of course, it is not related to any spectroscopic transition. So, we would not worry about it anymore in this course, ok. Now, what we are left with the is the remaining part h internal which is nothing but minus h bar square by 2 mu del square. Now, if I had a particle AB, ok and the this one was at x A, y A, z A, these are the coordinates of A, x B, y B, z B as internal coordinates of or coordinates of B, then you can write x is equal to x A minus x B, y is equal to y A minus y B, z is equal to z A minus z B and this represents internal. Therefore, your del square internal will be equal to d square by dx square plus d square by dy square plus d square by d z square, ok. And this is internal coordinates, ok. There is of course, one problem, ok. Now, the problem is this is that when you want to, so I know I told you that AB is a rigid body and of course, you must remember is R naught will be equal to x square plus y square plus z square to the power of half which will be nothing but x A minus x B whole square plus y A minus y B whole square plus z A minus z B whole square to the power of half, ok. Now, there is one thing that I told you is that of course, this can move in the x y z direction, the entire molecule AB. But I told you that we are not concerned with such a motion because it does not lead to any spectroscopic transitions, ok. Now, the thing that we are interested is in the rotational motion either in the plane of the board that I am writing or a plane that is perpendicular to the plane of the board, ok. When you have such motion, then, ok, then you are talking about a motion of AB in a circular fashion. So, that means, you can think of your H internal, your H internal is equal to minus h bar square by 2 m del square internal. And this internal motion is the motion of the molecule without moving the center of mass. So, what is this? This is that this is motion that does not change the center of mass, ok. So, you have to keep your center of mass fixed and then move around, ok. So, you have to rotate the body either in the plane of the board, ok. So, AB can rotate or rotate perpendicular plane of the board, ok, while keeping its center of mass fixed because the motion if there is a motion with of the center of mass that is already gone into the other Hamiltonian of the free particle that I discussed little bit earlier, ok. So, now, what we are concerned is just the motion of this AB molecule in a such a way that its center of mass remains exactly in the same position. So, this will lead to rotation. So, your H internal, ok, this is equal to minus h bar square by 2 m del square internal, which is nothing but minus h bar square by 2 m d square by dx square plus d square by dy square plus d square by dz square. This should represent rotation without changing, ok. We will stop here and continue in the next class. Thank you.


Hi, please introduce yourself. Hi, I'm Joe Speed with Ampere Computing. And this is your chip right here. This is doing a lot of noise in the industry right now. What are you looking at? It is. It's a 128 core CPU with 128 PCIe lanes. And these are used in everything from Google Cloud to vehicles and 5G base stations. And so it's a big chip. It's a lot of cores. And it's also a lot of performance. Yeah, it's a lot of performance, but it's a lot of efficiency. So this is three times the performance per watt of x86, like legacy architectures. It's three times. That's a lot. Quite a bit, yes. That's like a big shift. And here in the embedded world, the argument is that you want to bring this to the edge. That means potentially millions of devices. We would settle for millions. Millions would be okay. That's millions of base stations, 5G. Yeah, so 5G base stations, smart city, industrial automation, mill arrow, and all kinds of other applications. What are we looking at here? You can explain. What is this? So this is an industrial PC. So this is a fanless, ruggedized industrial PC with the ampere processor. And with optional NVIDIA GPUs, you can have an ampere CPU with your NVIDIA ampere GPU. This is a full self-contained, the whole thing. Yeah, yeah, yeah. And then this, this is a AI computer for autonomous race cars. Autonomous race cars. Yes. So soon, Formula One? We'll be robots. We'll see. But it's not really, people ask sometimes, like is it about humans competing against machines? It's actually, so I've been a long time involved with a program called the Indie Autonomous Challenge. And these aren't in the vehicles yet. We want to work with our friends DeSpace to put ampere into theirs. And they, and so it's a university software contest. When you put a 128 core in a car, you get a lot of potential self-driving happening in there? Well, it's really a software problem. But we help. We do our part. Because is that also the market for this one? Going to cars? So probably not all the cars, but I think they're very good for R&D fleets, for specialty vehicles. Autonomous people movers, earth-moving, mining, agriculture, things like that. Can you hold this board? Oh, sure. And try to explain what are we seeing on this board? So this launched here today. So this is a dev kit. This is the Ampere Ultra Dev Kit. It's made by our friends at AD Link. And you can visit them over in Hall 3. And it comes with the memory, the heat sink, storage, really all the things you need to do to, like if you're making devices, okay, and things, and you need to develop device drivers and tell us them. Or you need to see how easily your software can port to this architecture. This is the kind of thing that you would use. What do you connect here? So this is 3 PSI by 16. So you could, for example, put like 2 full-length, full-height NVIDIA GPUs plus a camera frame grabber. 3 GPUs. Well, you could. That's all of that. Yeah, yeah, yeah. 2 what? This is 2 PCIe by, I think 4 or 8. I don't know, I have to check. So you could put like NICS, network inner, network cards and other things. And what's connected up here? So we have more GIGI, GIGI, USB, VGA, serial. Just normal things. Nothing exotic. Yeah, it's, we kind of work hard to make the arm as boring and painless as X86 is in the data center. It's been a decade-long work, work in progress to optimize the arm for servers. And now would you say it's 100% fully smooth and actually even smoother than other architectures? I don't have a smoother, but it's widely deployed. You look at the Oracle cloud, Azure cloud, Google cloud, people like Hatsner, Equinix. You look at products from people like HPE and Supermicro. It's very much mainstream now.  They want to figure out how do they make their compute footprint green and sustainable and reduce the carbon impact. And so we help with all of those things. And now we're bringing those same benefits to the edge. If I go on Google cloud or Azure, I can click and I can get an ampere instance. Oh, sure. And it's great to work with. And it's a good price compared to some of the other architectures. And the Oracle cloud, they've been doing impressive things. They're running like large-scale heavy AI workloads on ampere on the Oracle cloud. So that's pretty interesting as well. And here you are, I would say right here, there was some, you know, right here. These are the different booths where you are. AdLink, AWS also, Automotive, Lattice. Yes, AWS Automotive. If you look at the Yachto project. So the Yachto project is using ampere for all the things that are demonstrating there in the booth. I'm working with AWS cloud and with the other Yachto project members. When I hear about the Yachto, I think about small stuff, right? But it goes all the way to 128 cores? Yeah, Yachto has grown up. So it's happy to work all the way up? Yes, yes, very much. What's the challenge when you make software and you want to use so many cores? You need to be very good at part of the processing in your software? Actually, you know, in the open source community, a lot of people take care of that for you. So you've got so many workloads that are being developed for the cloud and by their nature, they tend to scale out across many cores. And so any of that software that's designed to scale out across many servers is very happy running in one SOC using all 128 cores. One thing that I guess is a challenge but it's hard to talk about is the price of a chip. But that's not the main question, right? That is the price of the power to run the chip. Sure, so there's a few things. You can look at things like performance per dollar, you can look at total cost ownership. For the data centers, the exercise is how can I get more compute per rack while using less power, okay? And then you save so much. Well, now we're bringing those same kinds of benefits to the edge where if I can get more compute into a device and use less power, that's very good, right? Before you said 3x. Yeah. And that was power per... Performance per what? Performance per what? Yep, but also if you look at things... It's also translating dollars? Oh, yeah. When you look at the TCO, sure. And that was also looking at things like, you know, how much compute per rack or how much compute per device, okay? That I can get in the same power budget. So there's two ways to think of it. I can get triple the compute in the same power budget, or for the same compute, I can get using only one-third the same power. And so that's a really big savings and that lets you make very powerful devices that really don't use that much electricity. So that's a great benefit. So we see, you know, for like 5G base stations, there's like 30 companies working around 5G base stations, software accelerators, the whole stack integrated, and they're using our processor and that.  Maybe they could be driving around with the little base stations. And when there's an event, they go and add 5G capacity at the same time as, you know, they sleep in the van or something. Maybe this is this kind of stuff, maybe that could go in the van. Yeah, yeah, definitely so. So there's people making some small, powerful things with us and the benchmarks I've seen around like 5G base stations is moving from x86 to ampere. For the same power, they're getting double the throughput. Could you use a Starlink and make a 5G base station out of the Starlink connection, I guess, maybe? And then suddenly, oh, it's just, I don't know, it's a weird question. Okay, and here with the 7 Starlink, what kind of work are they doing with you? Yeah, so they work with our other partners, people like 80 Link and Gigabyte and Supermicro around taking these compute modules and motherboards with the ampere processor and making these rugged, extreme rugged, frankly, compute for different kinds of applications. So, you know, think like buses, trains, ships, subs, all kinds of things. Wow, what happens when you put this on the bus? You're able to do more using less power, so if you want to do like a safety or traffic safety with cameras, you can do all kinds of interesting things. There could be cameras inside the bus. Make sure everybody's, I don't know, I'm not going to say distancing, but they're all comfortable. Everybody's getting what they want. Yeah, yeah. And they get a VIP service on the bus, maybe. Or are these kind of like developing boxes? No, this is for development. These are very much for production deployments. So, they're actually made by 7 Starlink. What's the difference between this and this? I'll just see more cooling. Yeah, so what this has, you see the two fans that, fans, this is sealed, but the two fans blow air across the fans so it has more cooling so you can put a more powerful processor and a more powerful GPU. Cool. All right. So, you were the Mobile World Congress. It was a big story, right? Yeah. To run the base stations, you're all about doing that. And there's a big demand for that. Yes. And here in the embedded world, is this different kind of demand? Yeah. It's a different domain, a different application but some similar challenges. So, what's happening is, you're getting more and more sensors and the sensors are becoming higher and higher bandwidth and the communications is becoming higher bandwidth. And so, the challenge, but you don't always have, you might be constrained, you might have a fixed amount of physical size, weight and power. So, the swap available for your application. So, it becomes this issue of, how do I get more compute into my existing size, weight and power? All right. So, it's very relevant for everybody here who look for being at the extreme edge. Yes. Like the high performance edge. Exactly. Yeah. And we don't do, everyone talks about the 128 core, but we also go down to, you know, 32 core, right? With below 40 watt. You know, we can actually cover a pretty wide range of compute requirements and power budgets, but even at 32 core, we're giving you 128 PCIe lanes. So, all of your IEO. So, if you need to do like portable NAS or any other kind of application that's really demanding or CDNs, that's a popular application for these. You can, we have some very, we have some much lower power, highly efficient parts that you can use for that as well. This is a lot of partnerships in the software world. Not just Yocto. Yeah, yeah. So, we do a ton with like Canonical, with Red Hat, with a lot of ISVs. We give heavily to the open source community. So, we have hundreds of engineers who contribute to open source full time. So, we have open source BMC, open source firmware. So, the Tiana Core, EDK2, open BMC, all these kinds of things. And a lot of upstream contributions to things like GCC and LLVM. So, all the work that the guys at Linaro and elsewhere have been working on for a decade. Yeah, yeah, yeah. To be able to take all this and improve and give back to everybody. Well, I wouldn't even say we take it. We just contribute upstream. We're just trying to help the community and help everything. So, you get this painless out-of-the-box experience, okay? And everything just works. All right. But even though it just works, it's still a challenge to learn how to use so much parallel processing. Or is that just something that everybody that works in the cloud knows how to do? Yeah, it's... And it's not necessarily just about, like, how do I get one process, one workload to use a 128-core? You have a lot of mix. So, think about if you do a robot, okay? You've got Perception Pipeline, Path Planning, Control. You have all of these different workloads and being able to run them all on the same SOC, which in the past, maybe it was you would need many separate computers. So, if you have an application that today is requiring many separate computers, you can collapse that into one chip. And I guess your colleagues are very busy on the next gen and getting smaller and smaller nanometers and everything. So, there's a new product that will be announced at some point. But it's not a matter of, this is V1 and that's V2. That actually has some, you know, interesting characteristics. And so, this is, you know, for the edge for these kinds of things, this is very compelling. Nice. That's awesome. And eventually, maybe even the governments will get involved and start saying, hey, cloud is a certain part of our energy consumption in the country and the cities. And they will start saying, I'm not going to say they're going to mandate our servers, but they're going to tell everybody to be conscious and use less power. Yeah, so I certainly won't pretend to speak for government policy or any of those things. I will say that we help companies do more with the existing data centers and minimize their impact on the local communities. Cool. All right. Thanks a lot. Hey, real pleasure. Thank you.