>> From Boston, Massachusetts, it's the Cube covering WTG Transform 2019. Brought to you by Winslow Technology Group. >> Welcome back. I'm Stu Miniman, and we're here at WTG Transform 2019. Happy to welcome to the program first time guest, Marty Sanders who's the Chief Security Services Officer at Arctic Wolf. Marty, thanks so much for joining us. >> Thank you, Stu. >> All right Arctic Wolf's a partner, but before we get there, I have to say welcome back. >> Thank you, thank you. >> Because you're familiar with this event quite well. You have a background at Compellent, which of course we were just talking to Scott Winslow. It's where his company started. Just give our audience a little bit thumbnail of your background. >> Perfect. So yeah, Scott and I go back a long time. We actually started back working together at Zylotech back in the late 90's. After we left Zylotech, we actually went to Compellent. We started building Compellent back in 2002. As a company we wanted to start a new philosophy. Really sit down with customers prior to actually releasing products. So we actually built a customer council. We started that in Minneapolis, and then what we wanted to do is take it to the next level. We wanted to replicate that out to other parts of the country, and the first person we called was Scott. We started to do it with Scott, and started back in 2004. Had the first meeting here at the Commonwealth, actually with a handful of customers, and now it's grown into this. So it's unbelievable what he's done with the company. And when I look at what he does, he provides a tremendous amount of value to the customers and just sells them exactly what they want. But what they need as well. >> Yeah we always know when certain segments of the market that degree of separation, you look on LinkedIn is like, one and a half. >> Absolutely. >> Everybody knows each other. We all run around some of the same circles. So bring us up to speed. Arctic Wolf. I believe you're the first person we've had on from the company. So give us a little bit kind of the who and the what and the why. >> Perfect. ^- [Stu] Of Arctic Wolf. >> And again thank you very much for inviting us out for this as well. Yeah Arctic Wolf has been around since 2012. Started off in the SOC as a service. Obviously, in that small-medium business, they didn't have the capabilities to do a lot of the security work. Actually, Brian NeSmith, our CEO, started the company with his other founder Kim Tremblay. They worked at Blue Coat, they understood the security world. But understood that there was a big hole in that space, in that small-medium enterprise business. So they were actually way ahead of their time. I mean you look at from 2012 to 2015, it was a little bit slow growth. But now you start to look at where we're at, and the adoption of that, having a SOC as a service 7 by 24, hasn't been adopted very well. >> Yeah, I thought it was rather telling, actually in the keynote this morning, some people were asking about security, and they're like, wait, if I do this hybrid cloud stuff, how does that work? And I'm like, yeah I go to too many events. It's like, I have ingrained in my system now security is everyone's problem. There is no such thing as a moat. You assume that they are going to get in, so therefore I need to build at every level of the stack. I need to get in. But I'm an industry watcher. ^- [Marty] Yep. >> The people that are doing, what's their mindset, what's workin' well for them? Is security heightened? How's Arctic Wolf going? >> And you want to take that premise. I mean, one of the things that we do is we actually assign a concierge security team to that customer. So we want to be that extension of their environment. I mean, in fact, as we started to talk to some of the clients that we have here, they're repeating the words, what they feel like. My team is part of their team. And it makes it so much easier. So you're not dealing with somebody fresh every time that you call in. If you have any type of event that validates that there's somebody trying to break in. You want to have that person that understands your environment. Understands exactly where you've been. Making sure that you're up to speed on their network, all their ingress/egress points that they can come into. So it makes it so much easier if you have that consistent face that you're dealing with. >> Okay. Marty, is there a typical customer of Arctic Wolf? Where do you fit in the WTG? Their customer base? >> Yeah, I mean, that's a great question. I mean, when you look at where we really fit is, the first questions that we want to ask is do you have a security team? Do you have it 7 by 24? I mean, that's where we really want to make sure that we're augmenting that. I mean, when you look at a lot of the companies they might have that office admin that became the IT person, that became the security person. What we want to do is make sure that we're providing the true level of high security for those companies 7 by 24. Because obviously the bad guys know that there's going to be a hole after hours or whatever it's going to be. So that's when they want to go in. So we want to make sure that we're covering that. So Scott and his clients are kind of in that medium to small-medium business, moving up into the small enterprise, and it fits really well with them. >> Yeah, so you're saying most of them don't have an entire security SWAT team. >> Exactly. ^- Waiting 7 by 24, to do that. Walk us through maybe if you have a customer example or kind of a genericized version that you can share. What does an engagement look like from when they first plug in to when they're fully engaged? >> Perfect. So typically what we do is we actually once the deal is closed what we want to do is sit down with the customer and understand exactly all their different applications, all their environments. Understand all their ingress/egress points that they have coming in. We want to make sure that we're maximizing coverage. And what we want to do is triangulate anything that comes into that. Understand all the attack vectors that the bad guys may try to come in. So it takes us about 30 days to go through all of that. So once we get them onboarded, we assign that concierge security team. Going to be a senior and a less-senior person dedicated to that team. And basically they're going to go through and review that environment, make sure that they understand all the different applications. Is it Office 365? Any cloud apps that we need to hook up to it? All the different servers to make sure we're getting all that information. We want to provide more quiet service. We don't want to be, anytime someone knocks on the door, we don't want to be calling, Little Red Hen-type stories. We want to make sure that anything that we actually report on is going to be actionable for those customers. So that's that trusted confidante, that's where we build that strong relationship rather than sending out a note and retracting it as a false positive or anything like that. >> Okay. And Marty, I heard you mentioned some SAS applications and their infrastructure environment. Is public cloud included in that also? >> Absolutely. And what we want to do is make sure that we understand, like you said. And like Joe and Rick went through and talked about. There's going to be that private and public cloud. We want to make sure that we're capturing everything internally, but also if you're using those SAS applications on the outside, whatever they may be, we want to make sure that we're capturing all that information so that we can help with that. >> Okay. And billing. Is there multi-year commitments? Or how does the financial piece of this work? >> It can be MRR. I mean, we're going to go through on a monthly basis and we'd like to get at least a year commitment. It can be something that they sign up for a couple of months or they sign up for a year and pay monthly whatever they need to do. But typically what we want to do is provide that level of service and when you think about it, if you were to go out and buy a security team to cover 7 by 24, it's at least a minimum of six, seven people to do that. So when you look at the price point, we want to be less than that. We want to provide that high level of value. When you think about a single team going out and trying to do something, the typical threat is it has been in their environment for at least 100 days before they notice it. What we want to do is get it down to minutes. We want to make sure that any threat that's coming in we're notifying on it immediately. We want to make sure that we're going to capture all those things. >> All right. So Marty, when I talk to the big enterprises, security it's not only top of mind it's often a board-level discussion. When you come down to kind of the mid-size to small companies, where does security fit in their overall pictures? What are some of the biggest things on their mind? >> So it's very interesting. When you start to think about it, one of the things that is challenging, you look at some of the places that were having the greatest adoption rates are those companies that have the biggest threats. You look at where the money is. You look in the healthcare environments. The smaller healthcare. Or you look at the legal side of things. I mean, people know where there's money and where they need to have that data. So when you look at it, it's becoming a higher topic and it's becoming every conversation. And we don't like to say that the conversation gets highlighted after a breach or whatever it's going to be, but it does. I mean, and we'll be in the middle of some discussions and you'll hear about somebody that just got hit in a similar environment. And that's how then it gets brought up. >> Oh, boy. Sounds almost all the discussion is data is the new oil. >> Yes. Well those bad actors out there know where the oil is. >> Absolutely >> And therefore that's a security risk for them. >> Absolutely. And I mean the thing that you look at is, you hear about where some of the Atlanta, and some of the other cities that were hit. I mean they go after the localities and the municipalities of making sure that they're going after. And they know that they're going to pay very quickly because of how incredibly important that data is to do that. And even some of the sitting talking to some of the customers here today. Manufacturing, you know? Just the ability to go in and steal the IP that they have to make their business a little bit unique. That's where the people are concentrating because they want to take that and find that uniqueness in that business. >> All right. Marty, want to give you the final word. WTG Transform 2019. Talk about the partnership, talk about the customers and final takeaways. >> So the partnership, I mean, obviously Scott and I have known each other for a long time. The entire sales team and I know Scott. Rick Gowan actually was a customer of ours at Travelers Insurance. Scott hires great people, great employees. They partner. They take care of their customers better than anybody that I know. I mean, I just love the passion. In fact, some of the customers that we started with back in 2004 are still here. Still using the same products. But they continue to look at what provides the most value for them. >> All right. Marty Sanders the CSSO of Arctic Wolf, thanks so much for joining us. ^- Thank you, Stu. >> And appreciate all the updates. >> Thank you. All right. Full day of coverage here in the shadow of Fenway Park, Boston, Massachusetts. The East Coast team's home game as we like to say. I'm Stu Miniman. Thanks so much for watching the Cube. (gentle techno music)

>> live from Mountain View, California It's the queue covering definite create twenty nineteen. Brought to You by Cisco >> Welcome back to the Cube. Elisa Martin with Set Cisco Definite Create twenty nineteen at the Computer History Museum, but here all day, talking with some really great innovative folks excited to welcome to the Cube. Marty Jane, senior director of this Cisco Global Partnership and Video. Marty, It's great to have you here. >> Thank you. Good to be here. >> So I always love talking about partnerships Where what Day One of Dev. Net. Tomorrow's day to. There's been a lot of a lot of community spirit is here, so I just kind of in the spirit of partnerships, lot of collaboration that community is is really strong. Uh, before we get into kind of the details of this Cisco in video partnership first kind of thing, I wonder is all right. This is the developer community. Why the developer community within video? >> That's a great question. So if you think about way, make GP use, which is a piece of silicon graphics processing unit, and it is really only a piece of silicon until a developer comes along and develops a cool app on it. So if you think about how we go to market our large conferences called GTC, it's really developer. Focus. We have a little over a million developers in our ecosystem, and I find it very synergistic with Cisco. If you think about Suzy, we's vision. I think it's the same idea. You look at over half a million developers in their ecosystem and they want to develop collapse, and that's how your platform becomes relevant. So if you think of all the modern innovation that's coming from developers, so these are the folks that we should be talking to on a daily basis. I see a lot of commonality, a lot of synergies. In fact, we had Sisko definite come over to our conference GTC, and they they appeal to our developers. And now we're here talking to their developers and also developing some joint platforms which the the folks can use for. Like I said, the more modern *** with all the new data that's coming, whether the coyote with a machine learning automotive, smart cities, you name it, we need to be able to provide the platform to the developers >> and a number of those topics came up today, even during the keynote, Smart cities being able to utilize and accelerate work leads with a I and machine learning. They gave some great examples during the keynote of how developers can build networks. They give this cool example of I think it right off the hills of Coachella of designing a secure network for an indoor concert, designing it for an outdoor festival, Coachella and then designing it for a massive stadium like a big football game like the Super Bowl, for example. And they showed it that higher end. They showed how they're using machine, learning to zoom in on. For example, they had this little red box and you see people and what's actually in there than the machines detected was a fight and in real time, analysing this data and thence, dispatching the appropriate security to come and obviously probably take the drinks out of their hands first. But it was a really interesting, great real world example. So you guys have been partners a long time. Our you've been actually working at various companies with Cisco for a long time, but I think of Cisco and video coming together. How are you great? Something to accelerate these? Aye. Aye. And machine weren't were machine learning workloads that we're starting to see in every industry. >> You bet. Great question. So let me first comment on what you said about smart cities. I like to think of it as smart and safe cities. So actually, the first set of application will be around public safety. What the example you were giving his spot on? If you have large crowds gathering, it makes sense for us to be able to look at those clouds. Crowds? We call it intelligent video analytics or idea. In fact, we have a platform here. The Sisko i R eleven o one with a GPU added to it. So now I can wash the crowds. And if there's a fight breaking out or somebody's carrying in a weapon, you want to know somebody walks in carrying a backpack and drops it and moves on. You want to know one? Inform somebody. So what is happening is way of these millions and millions of bites of video data, >> and >> that data is not being really used today. So what we're doing is saying you know what? Let's find those pieces of intelligence and the video data and do something with it. And public safety is absolutely the highest priority. So smartest, safe city makes a lot of sense. So what we're doing is we're going to market with partners at Cisco. So what we're doing is we're saying Okay, let's design these GPS into the servers, which are connected to cameras and think about how many cameras are deployed today, probably a billion. And a lot of the video data can now be used for public safety purposes, and we basically go out and talk to large companies. We talked to governments. We talked to cities along with Sisko to go even open their eyes to what is possible today. >> Right? Because of that data is dark for so long, they don't know what they don't know. >> While most cases, what happens is you record four days of video and until something happens, nobody goes back and takes a look at it. But now we have the ability to look at the real time and cities and government's desire that very much so, >> sir example, that's such a relevant topic. I mean, they know. There's also the issue of privacy. But to your point about not just a smart city but a smart, safe city. I like that. I think it's absolutely imperative. How do you have this conversations with cities with governments about All right, this is what we want. Do we want to actually apply machine learning? So the machines are taught What that line is with privacy with those boundaries are so that a person, I'd say a lay person not in technology. Maybe is a city government official who doesn't understand the technology or need Teo will go. I get it. >> Yes. So our conversations are really about what we call you cases. So think of enterprise. A good use case would be. In fact, we work with Cisco on developing use case. You know, you always badge in into an enterprise. You have your badge, you walk in. But you also have some cases. People follow you, following you in what stops you from following me into a building. And usually people are too polite to say no, you can walk in, but we've >> all had the video training or read the manual. We know we're not >> we're not supposed to bite, but >> then you're like, I >> don't just cultural, exactly. We just can't you know that. So now we have the ability. So we trained a in a network to say, Look, if Marty's badging in, only he's allowed to walk in. And if there's a second person walks in, I want to take put Little Red Square on that face and inform security that we have had more than one person walking. So these are some of the ways. So we talk about use cases. This is one use case crowd behavior. Analytics is another use case. You know, people were walking in the backpack, dropping it. Other use case would be something like Bar to Bart loses millions of dollars year because people jumped the turnstiles and Bart didn't really have a good way of of monitoring, measuring the losses until we put a camera and captured the number of people that were jumping. The turnstiles are going in through the handicap access, okay? They were losing ten times the dollar value of what we had thought. Wow. So this is how we start the conversation with use cases, you know? And what would you like to do? Being able to count the number of cars in intersection begin with counter number of pedestrians, so you could do traffic management better. That's the language we would use with cities and governments. And then we go deeper as you go through the implementation process. >> Well, that makes perfect sense going in the use case route, because you can clearly see in that example that you mentioned with Bart a massive business outcome and an opportunity to regain a tremendous amount of resource is that they could redeploy for whether it it's new trains, new trucks, etcetera than them, not realizing we're losing how much money. I think anybody when you could put the useless in that context of this is what you can expect as an outcome. They get it >> Absolutely. That's the really the only way to start the conversation than starting from bits and bytes. And this is the This is usually the case across industries. If you think about retail, for example, you know you go to a safe way to start talking about GPS and servers. That's not the great way to start, but they do have issues with shoplifting, for example. So how do you know a person is walking in, you know, through the checkout. And they have one item. Then there's a small item right here and they walk out with this. How do you monitor that? So now you can do that with the right kind of cameras that can capture. Look there Two items, not one. How do you know where shop are stopping Which aisle is the most popular? I'Ll How do you know that? Well, now you can have cameras would say, Look, we have red zones and Green Zone so you could do those kinds of things with modern ways of doing. I >> so interesting because it's so. I mean, the examples that you gave are so disparate, but yet they make so much sense was how how you're describing it rather than going into, you know, a grocery store in talking about GPS, which they might fall over with their eyes. Doing this >> right. >> You're actually putting in the context of a real world problem they've been experiencing since the beginning of time. Don't you understand? Only goodness and this is how we can use technology. It's the safe way becomes a technology company. They don't know it. What actually started packing their bottom line. >> That's right, And so even now, you know. So I have to take that and you extend that into How do you go to market? And it's something you wanted Teo Touch on. How do you go to market with Cisco's? How does ingredients is? Could do it together, right? So think of Cisco's sales teams who are talking to all these customers every day where their retailers, financial services, federal government, health care, you name it. So what we've done is we basically sort of taking all these industries and created the top three or four use cases we know are relevant to that industry, either for safety or for saving money's. For variety of their operational reason, we have narrowed it down to three or four five use cases and each of those target industries. So what we do now with Cisco teams that we would bring them into our facility or go to them and really talkto all those use cases and train them on Hey, look, this is what we do jointly, and that makes the conversation much easier. Then they will go and present to the customer and what's the customer gets an idea far this all possible. Now that starts a deeper level technology and server and GPU engagement. So this is one way we go up and talk to different customers. What's the school's >> second? About a bit. Marcus. Cisco is so enormous, they have a billion different. I'm slightly exaggerating products with but a lot of different technologies that form many different solutions. So I imagine your Cisco expertise over many years of working with Cisco's a partner for other companies. How do you once you get to that deeper level conversation, how do you bring this different groups within Cisco together? So that that solution conversation is one that really aligns to that use case and the customer doesn't get it? >> Yeah, that's a difficult question to answer. That's like, you know your work. It's just cause a large company. But I think I also think they're also very cells driven, and that's what drives the different groups to come together. In fact, some people called me the Connector because I've been working. Cisco's so long. I know people and definite I know people in sales. I know people in the server. BU, in fact, if you think about the The platform was talking about the i r eleven o one with the jets and GPU that came as a result. I was talking to the i o t bu result talking to Dev net our situation the definite he said. You know what? This is cool are gonna do this. Then we take that to the IOC Guys is Oh, this is cool. We can take that. Put it in this platform, and then I'm next. Actually, next week I'm talking to a sale. Seaman Cisco. They cover utilities. And this platform was profit for utilities. Even think about fire monitoring in a forest. How do you do, boy thousand? The people to just watch what happens. We can take a platform like that now and really deploy it in hundreds of places which could monitor fires or the starting off a fire. But yes, bringing them together. It is no easy task. It's fun >> where you are smiling. I like that. Marty the connector. Jane, thank you >> so much for >> joining me on the kid this afternoon. Fun conversation. I enjoyed it. >> Ofcourse. Thank you. Likewise. Thank >> you, Lisa Martin for the Cube. you're watching us live, Francisco Definite. Create twenty nineteen. This is the end of day one. Stick around, John. Failure on I will be back tomorrow to cover day too. Thanks for watching.

(gentle upbeat music) >> Hello everyone, and welcome back to KubeCon CloudNativeCon here in Detroit, Michigan. My name is Savannah Peterson, joined with John Furrier. John, we are in the meat of the conference. >> It's really in crunch time, day two of three days of wall-to-wall coverage and this next guest is running the show at CNCF, the OG and been in the community doing a great job. I'm looking forward to this segment. >> Me too. I'm even wearing... You may notice, I am in my CNCF tee, and I actually brought my tee from last year for those of you. And the reason I brought it, actually, I want to use this to help introduce our next guest is the theme last year was resistance realized, and I think that KubeCon this year is an illustration of that resistance realized. Please welcome Priyanka Sharma to the show. Priyanka, thank you so much for being here with us. >> Thank you for having me. >> This is your show. How are you feeling right now? What does it feel like to be here? >> It's all of our show. I am just another participant, but I am so happy to be here. I think this is our third hybrid in person back event. And the whole ecosystem, we seem to have gotten into the groove now. You know, the first one we did, was in LA >> Savannah: Yes. >> Where you have that shirt from. Then we went to Valencia, and now here in Detroit I could sense the ease in the attendees. I can sense that it just feels great for everyone to be here. >> Savannah: Yeah. >> And you guys, who were face to face in LA, but this is really kind of back face to face, somewhat normalized, right? >> Priyanka: Yeah. >> And so that's a lot of feedback there. What's your reaction? Because the community's changed so much in three years, >> Savannah: Yes. >> Even two years, even last year. Where do you see it now? Because there's so much more work to do, but it feels like it's just getting started, but also at the same time it feels like people are celebrating at the same time. >> Yeah. >> Kubernetes is mainstream, CloudNative at scale. >> Savannah: That feels like a celebration. >> People are talking about developer... more developers coming on board, more traction, more scale, more interoperability, just a lot of action. What's your thoughts? >> I think you're absolutely right that we are just getting started. I've been part of many open source movements and communities. This is... I think this is something special where we have our flagship project considered mainstream, but yet so much to be done right over there. I mean, you've seen announcements around more and more vendors coming to support the project in, you know, the boring but essential ways that happened I think this week, just today, I think. And so Kubernetes continues to garner support and energy, which is unique in the ecosystem, right? Because once something becomes mainstream, normally, it's like, "Okay, boring." (John laughs) But that's happening. And I think the reason for that is CloudNative. It's built upon Kubernetes and so much more than Kubernetes. >> We have 140 plus projects >> Absolutely. >> and folks have a choice to contribute to something totally cutting edge or something that's, you know, used by everyone. So, the diversity of options and room for innovation at the same time means this is just the beginning. >> And also projects are coming together too. >> Priyanka: Yes. >> You're starting to see formation, you're starting to see some defacto alignment. >> Priyanka: Yes. >> You're starting to see the- >> Priyanka: Clustering. >> Some visibility into how the big moves are being playing out, almost the harvesting of that hard work. >> Priyanka: Yes, I do think there is consolidation, but I would definitely say that there's consolidation and innovation. >> John: Yeah. >> And that is something... I genuinely have not seen this before. I think there are definitely areas we're all really focusing on. I talked a lot about security in my keynote because it continues to gain importance in CloudNative, whether that is through projects or through practices. The same, I did not mention this in my keynote, but around like, you know, continuous delivery generally the software delivery cycle, there's a lot coming together happening there. And, you know, >> John: Yeah. >> many other spaces. So, absolutely right. >> Let's dig in a little bit actually, because I'm curious. You get to see these 140 plus projects. >> Yes. >> What are some of the other trends that you're seeing, especially now, as we're feeling this momentum around Kubernetes? The excitement is back in the ecosystem. >> Yes. So, so much happening. But I would definitely say that like the underlying basis of all these projects, right? I brought that up in my keynote, is the maintainers. And I think the maintainer group, is the talent keeps thriving and growing, the load on them is very heavy though. >> Savannah: Yeah. >> And I do think there's a lot more we all company, the companies around us need to do to support these people, because the innovation they're bringing is unprecedented. Besides Kubernetes, which has its own cool stuff all the time. I think I'm particularly excited about the Argo projects. >> Savannah: Yeah. >> So, they're the quadruplets as I like to call them. Right? Because there's four of them within the Argo banner. I had Yuan from Argo on my keynote actually. >> Savannah: Oh, nice. >> Alongside Hiba from Kubernetes. And we talked about their maintainer journey. And it's interesting. Totally different projects. Same asks, you know, which is more support and time from employers, more ways to build up contributors and ultimately they love the CNCF marketing supports. >> That Argo project's really in a great umbrella. There are a lot of action going on. Arlon, I saw that. Got some traction. A lot of great stuff. The question I want to ask you, and I want to get your reaction to this, you know, we always go to a lot of events with theCUBE and you can always tell the vibrant of the ecosystem when you see developers doing stuff, projects going on. But when you start seeing the commercialization >> Priyanka: Yes. >> The news briefings coming out of this show feels a lot like reinvent, like it's like a tsunami. I've never seen this much news. Everyone's got a story, they got announcing products. >> Savannah: That was a lot of news. That's a great point, John. >> There was a lot of flow even from the CNCF. >> Yeah. >> What's your reaction to that? I mean like to me it's a tell sign of activity, certainly, >> Right. >> And engagement. >> Right. >> But there's real proof coming out, real visibility into the value propositions, >> Priyanka: Yes. >> rendering itself with real products. What's your reaction to the news flow? >> Absolutely. I think it's market proof, like you said, right? >> Savannah: Yeah. >> That we have awesome technologies that are useful to lots of people around the world. And I think that, I hope this continues to increase. And with the bite basket of project portfolios that's what I hope to see. CNCF itself will continue supporting the maintainers with things like conformance programs which are really essential when you are... when you have people building products on top of your projects and other initiatives so that the technological integrity remains solid while innovation keeps happening. >> I know from a little birdie, Brendan, good friend of mine that you had a board meeting today. >> Priyanka: Yes. >> And I am curious because I hope I'm not going out about an assumption I imagine that room is full of passionate people. >> Priyanka: Absolutely. >> CNCF board would be a wild one. (Priyanka laughs) What are the priorities for the board between now and KubeCon next year? >> Sure. So the CNCF governing board is an over... It's like an oversight body. And their focus is on working with us on the executive team to make sure that we have the right game plan for the foundation. They tend to focus on the business decisions, things such as how do we manage our budget, how do we deploy it, and what are the initiatives? And that's always their priority. But because this is CloudNative and we are all technologists who love our projects, >> Savannah: Yeah. >> we also engage closely with the technical oversight committee who was in the said meeting that we just talked about. And so lots of discussions are around project health, sustainability. How do we keep moving? Because as you said, Kubernetes is going mainstream but it's still cool. There are all these other cool things. It's a lot going on, right? >> Savannah: Yeah. You got a lot of balls in the air. It's complex decision making and balancing of priorities. >> Priyanka: Yes. >> John: And demands, stakeholders. You have how many stakeholders? Every project, every person, every company. >> Everyone's a stakeholder. You're a stakeholder, too. >> And a hundred... I mean, I love how community focused you are. Obviously we're here to talk about the community. You have contributors from 187 different countries. >> Priyanka: It's one of the things I'm the most proud of. >> Savannah: It's... Yeah. It gives me all the feels as a community builder as well. >> Priyanka: Yeah. >> What an accomplishment and supporting community members in those different environments must be so dynamic for you and the team. >> Absolutely, and it behooves us to think globally in how we solve problems. Even when we introduce programs. My first question is, are we by accident being, let's say, default U.S. or are we being default Europe, whatever it may be because we really got to think about the whole world. >> John: It's global culture, it's a global village. >> Priyanka: Yes. >> And I think global now more than ever is so important. And, the Ukraine >> Priyanka: Yes. >> discussion on the main stage was awesome. I love how you guys did that because this is impacting the technology. We need the diverse input. Now I made a comment yesterday that it's going to make... it might slow things down. I meant as is more diversity, there's more conversations. >> Priyanka: Yes. >> But once people get aligned and committed, that's where the magic happens. Share your thoughts on the global diversity, why it's important, how things are made, how decisions are made. What's the philosophy? Because there's more to get your arms around. >> Yes, absolutely. It may seem harder or slower or whatever but once it gets done, aligned and committed, the product's better, everything's better. >> Priyanka: Yes, absolutely. I think the more people involved, the better it is for sure. Especially from a robustness resilience perspective. Because you know, as they say, sunlight makes bugs shallow. That's because the more eyes on something the faster people will solve problems, fix bugs and make, you know, look for security, vulnerability, solve all that. So especially in those areas, I think, where you want to be more resilient, the more the people, the better it is. A hundred percent. And then when it comes to direct technical direction and choosing a path, I think that's where, you know it's the role of the maintainers. And as I was saying there's only a thousand audit maintainers for 140 plus projects, right? So they are catering- >> Wow, they have a lot of responsibility. >> Right. >> Serious amount of responsibility. >> It's crazy. I know. And we have to do everything we can for those people because they are the ones who set the vision, set the direction, and then 176,000 plus contributors follow their lead. So we have... I think, the bright mechanisms of contribution and collaboration in a global way are in place. And we keep chugging along and doing better and better each year. >> What's next for you guys? You got the EU of show coming out, >> Priyanka: Correct, Amsterdam. the economy looking, I don't see your recession for technology, but that's me. I'm Polish on tech. Yeah, there's some layoffs going on, some cleaning up, overinflated expectations on valuations of startups, but I don't see this stopping or slowing down. But what's your take? >> Priyanka: Yeah, I mean, as I said in my keynote, right? Open source usage soars in times of turmoil and financial turmoil is one example of that. So we are expecting growth and heavy growth this year, next year and onwards. And in fact, going back to the whole maintainer journey, now is a time there's even more pressure on them and companies as they manage their, you know, workforces and prioritization, they really need to remember they're building products off of open source. They are... This is open sources on which what their business realize, whether they're a vendor or end user and give maintainers a space time to work on what they need to work on. >> Yeah. They need a little work-life balance. I mean the self-care there, I can't even imagine the complexity of the decision matrix in their mind. Speaking of that, and obviously you... Culture must be a huge part of how you lead these teams. How do you approach that as leader? >> I think the number one... So the foundation is a very small set of staff, just so you know. >> Savannah: I was actually... Let's tell the audience, how many people are on the team? >> Priyanka: You know, it's actually a difficult question because we have folks who like spin up and down and we have matrix support from the Linux Foundation, but about 30 people in total are dedicated to CNCF at any given time. >> Savannah: Wow. >> But compared... >> Savannah: You all do hard work. >> Yes. >> Savannah: You're doing great. I am impressed. >> It's a flat organization. >> It's pretty flat. >> Seriously, it's beautiful. >> It's actually in some ways very similar to the projects and there the, you know, contribution communities there where it's like everyone kind of like steps up and does what needs to be done, which is wonderful and beautiful, but with the responsibility on our shoulders, it's definitely a balancing act. So first off, it is, I ask everyone to have some grace for the staff. They are in a startup land with no IPO on the other side of the rainbow. They're doing it because they love love, love this community and technology so much. >> John: Yeah. Yeah, and then also they're acknowledging that nobody in open source wants to see a bureaucracy. >> Priyanka: Right. >> I mean, everyone see lean, efficient. >> Savannah: Yeah, absolutely John. It's great. It's a great point. And and I think that it's just... It's amazing what passionate people can do if given the opportunity. Let's talk a little bit about the literal event that we're at right now. >> Priyanka: Yes. >> Theme today, building for the road ahead. >> Priyanka: Yes. >> What was the inspiration for that? >> Detroit. (group laughs) We're in Detroit, people drive here. >> Savannah: In case you didn't know, cars have been made in this city. >> Motor city. >> It's everywhere being here in this city, which is awesome. >> But you know, it did... There was of course a geographical element but it also aligns with where we're at, right? >> Savannah: Yeah. >> We're building for the road ahead, which frankly given the changes going on in the world is a bumpy road. So it's important to talk about it. And that's what the theme was. >> And how many folks have shown up... This is a totally different energy from Los Angeles last year. I'm sure we can both agree. Everyone was excited last year, but this is an order of magnitude. >> Yes. >> How many folks do you think are milling around? >> Yeah, it's much more than double of Los Angeles. We are close to 8,000. >> Savannah: That's amazing. And it's so... You're absolutely right. The energy is just... >> Savannah: Way up. >> It's so good. People are enjoying themselves. It's been lovely. >> That's great. So you're feeling good? You're riding the high? >> Congratulations. >> Awesome. >> Yeah, thank you. I mean, I'm a little bit of a zombie right now. (group laughs) >> You don't look it, we wouldn't know. Nobody knows. They don't know. >> If you want to take a break, We got 12 interviews tomorrow. (Savannah and Priyanka laughing) You can co-host with us. We'd love to have you. >> Exactly. You're welcome anytime. Welcome anytime, Priyanka. >> Well thank you. But no, it's been such a wonderful show and you folks are part of the reason you say everybody here is contributing to the awesomeness. >> John: Yep. >> You're part of it. Look at your smiley faces. >> John: And Lisa Marty is over there. Lisa's over there. >> Yes! >> Say hi to Lisa and team. >> Yes, the team is awesome. >> Guys, thank you for your support for theCUBE. We really appreciate it. We enjoy it a lot. And we love the community. Thank you. >> Yes. Thank you for your support for CloudNative. >> Thank you. >> One last thing I just want to point out, because it's not always it happens in this industry. The women outnumber the men on this stage right now. >> John: Proud of that? >> And I know the diversity and inclusion is a priority for CNCF. >> Priyanka: Top priority. >> Yeah. Can you tell us a little bit more about that? >> Yes. It is something at the forefront of my mind, no matter what we do. And it's because I have such great role models. You know, when I was just a participant in the ecosystem, Dan Conn was leading the foundation and he took it so seriously to always try to uplift people from a diverse backgrounds and bring those faces into CloudNative. >> Savannah: Yes. >> And he made a serious lasting impact. >> John: Yes. >> And I am not going to let that go to waste. It's not going to be me who drops the ball. (group laughs) >> We're behind you all the way. >> Right? >> We see improvement over here. >> We got your back. >> I mean, even from an attendance perspective on stage I feel like you've done just an outstanding job with the curation and representation. I don't say that lightly. It really matters to me. But even in the audience looking around, it's so refreshing. Even it sounds silly. The shirts are more fitted. >> It's not silly. >> There's different types of shirts, and I mean, you know how it is. We've been in this industry long enough. >> It's a shirt you want to wear. >> Savannah: Exactly. And that's the whole point. I absolutely love it. Have we announced a location for KubeCon North America 2023, yet? >> It's Chicago. >> Savannah: Exciting! >> Yes. >> Savannah: All right. So we'll be seeing you >> Midwest. >> not that far away. >> This is the first time I've said this publicly, I just realized, It's Chicago, people. >> The scoop, yay! >> Oh, I feel so lucky we got to break the scoop. I was learning from John's lead there and I'm very excited. Amsterdam, Chicago. It's going to be absolutely >> I'll get my hotel now. >> Fantastic. >> Yes. >> Smart move. Everybody listen to him. >> Yeah, right? Especially after Detroit. It's actually not a... It's not a bad move. Priyanka, is there anything else you'd like to say to folks? Maybe they're thinking about coming or contributing to the ecosystem? >> Priyanka: Yes. Anyone and everyone can and should contribute and join us. The maintainers are holding us all up. Let's rally to support them. We have more and more programs to do that. As you know, we did ContribFest here this week which was the first time. So we will help you get involved so you're not on your own. So that's my number one message, which is anyone and everyone, you're welcome here. We'll make sure you have a good time. So just come. >> Okay. Please do it. >> I can tell you that Priyanka is not blowing smoke. I feel very welcome here. This community has welcomed me as a non-technical, so I think you're absolutely preaching the truth. Priyanka, thank you so much for being here with us today on the show, for helping herd the cats and wrangle the brilliant minds that make CNCF possible. And honestly for just bringing your energy and joy to the entire experience. John, thank you for hanging out with me. >> I'm glad I can contribute in a small way. >> I was going to say... I was going to say thank you for founding theCUBE so that we could be here in this little marriage and collaboration can be possible. And thank all of you for tuning in to theCUBE here, live from Detroit, Michigan. My name is Savannah Peterson. I am thrilled to be sharing this content with you today and I hope to see you for the rest of our interviews this afternoon. (gentle upbeat music)

>>Hey, welcome back everyone to cube Cod's coverage and cloud native con the I'm John for your husband, David Nicholson cube analyst, cloud analyst. Co-host you got two great guests, KIPP alumni, Jerry Chen needs no introduction partner at Greylock ventures have been on the case many times, almost like an analyst chair. It's great to see you. I got guest analyst and Martin mal who's the CEO co-founder of Chronosphere just closed a whopping $200 million series C round businesses. Booming. Great to see you. Thanks for coming on. Thank you. Hey, first of all, congratulations on the business translations, who would have known that observability and distributed tracing would be a big deal. Jerry, you predicted that in 2013, >>I think we predicted jointly cloud was going to be a big deal with 2013, right? And I think the rise of cloud creates all these markets behind it, right. This, you know, I always say you got to ride a wave bigger than you. And, uh, and so this ride on cloud and scale is the macro wave and, you know, Marty and Robin cryosphere, they're just drafted behind that wave, bigger scale, high cardinality, more data, more apps. I mean, that's, that's where the fuck. >>Yeah. Martin, all kidding aside. You know, we joke about this because we've had conversations where the philosophy of you pick the trend is your friend that you know, is going to be happening. So you can kind of see the big waves coming, but you got to stay true to it. And one of the things that we talk about is what's the next Amazon impact gonna look like? And we were watching the rise of Amazon. You go, if this continues a new way to do things is going to be upon us. Okay, you've got dev ops now, cloud native, but observability became really a key part of that. It's like almost the, I call it the network management in the cloud. It's like in a new way, you guys have been very successful. There's a lot of solutions out there. What's different. >>Yeah. I'd say for Kearney sphere, there's really three big differences. The first thing is that we're a platform. So we're still an observability platform. And by that, I mean, we solved the problem end to end. If thinking about observability and monitoring, you want to know when something's wrong, you want to be able to see how bad it is. And then you want to able to figure out what the root cause is. Often. There are solutions that do a part of that, that that problem might solve a part of the problem really well for a platform that does the whole thing. And 10 that's that's really the first thing. Second thing is we're really built for not just the cloud, but cloud native environments. So a microservices architecture on container-based infrastructure. And that is something that, uh, we, we have saw coming maybe 20 17, 20 18, but luckily for us, we were already solving this problem at Uber. That's where myself, my co-founder were back in 20 14, 20 15. So we already had the sort of perfect technology to solve this problem ahead of where the, the trend was going in the industry and therefore a purpose-built solution for this type of environment, a lot more effective than a lot of the existing. >>It's interesting, Jerry, you know, the view investing companies that have their problem, that they have to solve themselves as the new thing, versus someone says, Hey, there's a market. Let's build a solution for something. I don't really know. Well, that's kind of what's going on here. Right? It's >>That's why we love founders. Like Martin Marna, rod that come out with these hyperscale comes Uber's like we say, they've seen the future. You know, like there were Uber, they looked at the existing solutions out there trying to scale Promethease or you know, data dogs and the vendors. And it didn't work. It fell over, was too expensive. And so Martin Rob saw solid future. Like, this is where the world's going. We're going to solve it. They built MP3. It became cryosphere. And um, so I don't take any credit for that. You know, I just look fine folks that can see the future. >>Yeah. But they were solving their problem. No one else had anything. There's no general purpose software that managed servers you could buy, you guys were cutting your teeth into solving the pain. You had Uber. When did you guys figure out like, oh, well this is pretty big. >>Uh, probably about 20 17, 20 18 with a rise in popularity of Kubernetes. That's when we knew, oh wait, the whole world is shifting to this. It's not, no one could really it to just goober and the big tech giants of the world. And that's when we really knew, okay. The whole, the whole whole world is shifting here. And again, it's, it's sheer blind luck that we already had the ideal solution for this particular environment. It wasn't planned it. Wasn't what we were planning for back then. But, um, yeah. Get everything. >>It makes a lot of difference. When you walk into a customer and say, we had this problem, I can empathize with you. Not just say we've got solved. Exactly. Jerry, how do they compete in the cloud? We always talk about how Amazon and Azure want to eat up anything that they see that might, you know, something on AWS. Um, this castle in the cloud opportunity here. Okay. >>In the cloud. I mean, you know, we talked last time about how to fight the big three, uh, Amazon Azure and, uh, and Google. And I think for sure they have basic offerings, right. You know, Google Stackdriver years ago, they've done basically for Pete's offerings, basic modern offerings. I think you have like basic, simple needs. It's a great way to get started, but customers don't want kind of a piecemeal solution all the time. They want a full product. Like Datadog shows a better user experience, but full product is going to, you know, the better mousetrap the world will beat a path to your door. So first you can build a better product versus these point solutions. Number two is at some scale and some level complexity, those guys can handle like the demanding users that current affairs handling right now, right? The door dash, the world. >>And finally don't want the Fox guarding the hen house. You know, you don't want to say like Amazon monitoring, you can't depend on Amazon service monitoring your Amazon apps or Google service monitor your Google apps, having something that is independent and multi-cloud, that can dual things, Marta said, you know, see a triage, fixed your issues is kind of what you want. And, um, that's where the market's skilling. So I do believe that cloud guys will have an offering the space, but in our castle and cloud research, we saw that, yeah, there's a plenty of startups being funded. There's plenty of opportunity. And that the scoreboard between Splunk Datadog and all these other companies, that there's a huge amount of market and value to be created in this piece. So, >>So with, at, at the time, when you, you know, uh, uh, necessity is the mother of invention, you're an Uber, you have a practical problem to solve and use you look around you and you see that you're not the only entity out there that has this problem. Where are we in that wave? So not everyone is at, cloud-scale not everyone has adopted completely Kubernetes and cloud native for everything. Are we just at the beginning of this wave? How far from the >>Beach are we, we think we're just at the beginning of this wave right now. Um, and if you think about most enterprises today, they're still using on, and they're not even in perhaps in the cloud at all right. Are you still using perhaps APM and solutions, uh, on premise? So, um, if you look at that wave, we're just at the beginning of it. But when, but when we talked to a lot of these companies and you ask them for their three year vision, Kubernetes is a huge piece of that because everyone wants to be multi-cloud everyone to be hybrid eventually. And that's going to be the enabler of that. So, uh, we're just in the beginning now, but it seems like an inevitable wave that is coming. >>So obviously people evaluated that exactly the way you're evaluating that. Right. Thus the funding, right. Because no one makes that kind of investment without thinking that there is a multiplier on that over time. So that's pretty, that's a pretty exciting place. >>Yeah. I think to your point, a lot of companies are running into that situation right now, and they're looking at existing solutions there for us. It was necessity because there wasn't anything out there now that there is a lot of companies are not using their sort of precious engineering resources to build their own there. They would prefer to buy a solution because this is something that we can offer to all the companies. And it's not necessarily a business differentiating technology for the businesses themselves >>Distributed tracing in that really platform. That's the news. Um, and you mentioned you've got this, a good bid. You do some good business. Is scale the big differentiator for you guys? Or is it the functionality? Because it sounds like with clients like door dash, and it looks a lot like Uber, they're doing a lot of stuff too, and I'm sure everyone needs the card. Other people doing the same kind of thing, that scale, massive amount of consumer data coming in on the edge. Yeah. Is that the differentiation or do you work for the old one, you know, main street enterprise, right. >>Um, that is a good part of the differentiation and for our product thus far before we had a distributor tracing for monitoring and metric data, that was the main differentiation is the sheer volume of data that gets produced so much higher, really excited about distributor tracing because that's actually not just a scale problem. It's, it's a space that everybody can see the potential distributor tracing yet. No one has really realized that potential. So our offering right now is fairly unique. It does things that no other vendors out there can do. And we're really excited about that because we think that that fundamentally solves the problem differently, not just at a larger scale, >>Because you're an expert, what is distributed tracing. >>Yeah. Uh, it's, it's, it's a great question. So really, if you look at this retracing, it captures the details of a particular request. So a particular customer interaction with your business and it captures how that request flows through your complex architecture, right? So you have every detail of that at every step of the way. And you can imagine this data is extremely rich and extremely useful to figure out what the underlying root causes of issues are. The problem with that is it's very bit boast. It's a lot of data gets produced. A ton of data gets produced, every interaction, every request. So one of the main issues are in this space is that people can't afford cost effectively to store all of this data. Right? So one of the main differentiators for our product is we made it cost efficient enough to store everything. And when you have all the data, you have far better analytics and you have >>Machine learning is better. Everything's better with data. That's right. Yeah. Great. What's the blind spot out. Different customers, as cybersecurity is always looking for corners and threats that some people say it's not what you want. It's what you don't see that kills you. That's, that's a tracing issue. That's a data problem. How do you see that evolving in your customer base clients, trying to get a handle of the visibility into the data? >>Yeah. Um, I think right now, again, it's, it's very early in this space of people are just getting started here and you're completely correct where, you know, you need that visibility. And again, this is why it's such a differentiator to have all the data. If you can imagine with only 10% of the data or 1% of data, how can you actually detect any of these particular issues? Right. So, uh, uh, data is key to solving that >>Feel great to have you guys on expert and congratulations on the funding, Jerry. Good to see you take a minute to give a plug for the company. What do you guys do? And actually close around the funding, told you a million dollars. Congratulations. What are you looking for for hiring? What are your milestones? What's on your plan plan. >>Yeah. Uh, so with the spanning, it's really to, to, uh, continue to grow the company, right? So we're sort of hiring, as I told you earlier, we are, uh, we grew our revenue this year by, by 10 X in the sense of the 10 months of this year, thus far. So our team hasn't really grown 10 X. So, so we, we need to keep up with that grid. So hiring across the board on engineering side, on the go to market side, and I just continue to >>Beat that. The headquarters, your virtual, if you don't mind, we've gone >>Completely distributed. Now we're mostly in the U S have a bunch of folks in Seattle and in New York, however, we going completely remote. So hiring anyone in the U S anywhere in Europe, uh, >>Oh, I got you here. What's your investment thesis. Now you got castles in the cloud, by the way, if you haven't seen the research from Greylock, Jerry and the team called castles in the cloud, you can Google it. What's your thesis now? What are you investing in? >>Yeah, it is. It is hard to always predict the next wave. I mean, my job is to find the right founders, but I'd say the three core areas are still the same one is this cloud disruption to Martin's point we're. So early days, the wave, I say, number two, uh, there's vertical apps, different SAS applications be finance, healthcare construction, all are changing. I think healthcare, especially the past couple of years through COVID, we've seen that's a market that needs to be digitized. And finally, FinTech, we talked about this before everything becomes a payments company, right? And that's why Stripe is such a huge juggernaut. You know, I don't think the world's all Stripe, but be it insurance payments, um, you know, stuff in crypto, whatever. I think fintechs still has a lot of, a lot of market to grow. >>It's making things easier. It's a good formula right now. If you can reduce complexity, it makes things easy in every market. You're going to seems to be the formula. >>And like the next great thing is making today's crappy thing better. Right? So the next, the next brace shows making this cube crappy thing. Yeah, >>We're getting better every day on our 11th season or year, I'm calling things seasons now, episodes and season for streaming, >>All the seasons drop a Netflix binge, watch them all the >>Cube plus and NFTs for our early videos. There'll be worth something because they're not that good, Jerry. How, of course you're great. Thank you. Thanks guys. Thanks for coming on it. Cubes coverage here in a physical event, 2021 cloud being the con CubeCon I'm John farrier and Dave Nicholson. Thanks for watching.

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

>> Thank you for coming to this talk. My name is Amir Safavi-Naeini I'm an Assistant Professor in Applied Physics at Stanford University. And today I'm going to talk about a platform that we've been developing here that allows for quantum and classical information processing using photons and phonons or mechanical motion. So first I'd like to start off, with a picture of the people who did the work. These are graduate students and postdocs in my group. In addition, I want to say that a lot of the work especially on polling of the Lithium niobate was done in collaboration with Martin Fejer's group and in particular Dr.Langrock and Jata Mishra and Marc Jankowski Now our goal is to realize a platform, for quantum coherent information processing, that enables functionality which currently does not exist in other platforms that are available. So in particular we want to have, a very low loss non-linearity that is strong and can be dispersion engineered, to be made broadband. We'd like to make circuits that are programmable and reconfigurable, and that necessitates having efficient modulation and switching. And we'd also really like to have a platform that can leverage some of the advances with superconducting circuits to enable sort of large scale programmable dynamics between many different oscillators on a chip. So, in the next few years what we're really hoping to demonstrate are few photon, optical nonlinear effects by pushing the strength of these non-linearities and reducing the amount of loss. And we also want to demonstrate these coupled, sort of qubit and many oscillators systems. Now the Material system, that we think will enable a lot of these advances is based on lithium niobate, so lithium niobate is a fair electric crystal. It's used very widely in optical components and in acousto optics and then surface acoustic wave devices. It's a fair electric crystal, that has sort of a built-in polarization. And that enables, a lot of effects, which are very useful including the piezoelectric effect, electro- optic effects. And it has a very large K2 optical non-linearity. So it allows for three wave mixing. It also has some effects that are not so great for example, pyroelectricity but because it's very, established material system there's a lot of tricks on how to deal with some of the less attractive parts of it of this material. Now most, Surface Acoustic Wave, or optical devices that you would find are based on kind of bulk lithium niobate crystals that either use surface acoustic waves that propagate on a surface or, you know, bulk waves propagating through a whole crystal, or have a very weak weakly guided low index contrast waveguide that's patterned in the lithium niobate. This was the case until just a little over a decade ago. And this work from ETH Zurich came showing that thin-film lithium niobate can be, bonded and patterned. And Photonic circuits very similar to assigning circuits made from three fives or Silicon can be implemented in this material system. And this really led to a lot of different efforts from different labs. I would say the major breakthrough came, just a few years ago from Marko Loncar, where they demonstrate that high quality factors are possible to realize in this platform. And so they showed resonators with quality factors in the tens of billions corresponding to, line widths of tens of megahertz or losses of, just a few, DB per meter. And so that really changed the picture and you know a little bit after that in collaboration with Martin Fejer's group at Stanford they were able to demonstrate polling and so very large this version engineered nonlinear effects and these types of waveguides. And, and so that showed that, sort of very new types of circuits can be possible on this platform Now our approach is very similar. So we have a thin film of lithium niobate and this time it's on Sapphire instead of oxide or some polymer. and sometimes we put oxide on top. Some Silicon oxide on top, and we can also put electrodes these electrodes can be made out of a superconductor like niobium or aluminum or they can be gold depending on what we're trying to do. The sort of important thing here is that the large index contrast means that, light is guided in a very highly confined waveguide. And it supports bends with small bending radii. And that means we can have resonators that are very small. So the mode volume for the photonic resonators can be very small and as is well known. The interaction rate scale is, one over squared of mode volume. And so we're talking about an enhancement of around six orders of magnitude in the interaction length interaction lengths, over systems using sort of bulk components. And this is in a circuit that's sort of sub millimeter in size and its made on this platform. Now interaction length is important but also quality factor is very important. So when you make these things smaller you don't want to make them much less here. That's, you know, you can look at, for example a second harmonic generation efficiency in these types of resonances and that scales as Q, to the power of three essentially. So you need to achieve, you win a lot by going to low loss circuits. Now loss and non-linearity or sort of material and waveguide properties that we can engineer, but design of these circuits, careful design of these circuits is also very important. For example, you know, because these are highly confined waves and dielectric wave guides they can, you can support several different orders of modes especially if you're working for a broad band light waves that span, you know, an octave. And now when you try to couple light in and out of these structures, you have to be very careful that you're only picking up the polarizations that you care about, and you're not inducing extra loss channels effectively reducing the queue, even though there's no material loss if you're these parasitic coupling, can lead to lower Q. so the design is very important. This plot demonstrates, you know, the types of extrinsic to intrinsic coupling that are needed to achieve very high efficiency SHG, which is unrelated to optical parametric oscillation. And, you know, you, so you sort of have to work in a regime where the extrinsic couplings are much larger than the intrinsic couplings. And this is generally true for any type of quantum operation that you want to do. So just just low material loss itself isn't enough to design is also very important. In terms of where we are, on these three important aspects like getting large G large Q and large cap up. So we've been able to achieve high Q in, in these structures. This is a Q a of a couple million, we've also been able to you can see from a broad transmission spectrum through a grading coupler you can see a very evenly spaced modes showing that we're only coupling to one mode family. And we can see that the depth of the modes is also very large, you know, 90% or more. And that means that our extrinsic coupling in intrinsic coupling is also very large. So we've been able to kind of engineer these devices and to achieve this in terms of the interaction, I won't go over it too much but, you know, in collaboration with Marty Feres group we were able to pull both lithium niobate on insulator and lithium niobate on Sapphire. We'll be able to see a very efficient, sort of high slope proficiency second harmonic generation, you know achieving approaching 5000% per watt centimeters squared for 1560 to 780 conversion. So this is all work in progress. And so for now, I'd like to talk a little bit about the integration of acoustic and mechanical components. So, first of all why would we want to integrate mechanical components? Well, there's lots of cases where, for example you want to have an extremely high extinction switching functionality. That's very difficult to do with electro optics because they need to control the phase, extremely efficiently with extreme precision. You would need very large, long resonators and or large voltages becomes very difficult to achieve you know, 60 DB types of, switching. Mechanical systems. On the other hand, they can have very small mode volumes and can give you 60 DB switching without too many complications. Of course the drawback is that they're slower, but for a lot of applications, that doesn't matter too much. So in terms of being able to make integrate memes, switching and tuning with this platform, here's a device that achieves that so that each of these beams is actuated through the Piezoelectric effect and lithium niobate via this pair of electrodes that we put a voltage across. And when you put a voltage across these have been designed to leverage one of the off diagonal terms in the piezoelectric tensor, which causes bending. And so this bending generates a very large displacement in the center of this beam, in this beam, you might notice is composed of a grading, and this grading effectively generates it's photonic crystal cavity. So it generates a localize optical mode in the center which is very sensitive to these displacements. And what we're able to see in this system is that you know, just a few millivolts so 50 millivolts here shifts the resonance frequency by much more than a line width just a few millivolts is enough to shift by a line width. And so to achieve switching we can also tune this resonance across the full telecom band and these types of devices whether in waveguide resonator form can be extremely useful for sort of phase control in a large scale system, where you might want to have many many face switches on a chip to control phases with, with low loss, because these wave guides are shorter. You have lower loss propagating across them. Now, these interactions are fairly low frequency. When we go to higher frequency, we can use the electro-optic effect. And even the electro-optic effect even though it's very widely used, and well-known on a Photonic circuit like these lithium Niobate for tying circuits has, interesting consequences and device opportunities that don't exist on the bulk devices. So for example, let's look at single sideband modulation. This is what an electro-optic sort of standard electro optics, single sideband modulator looks like you, you take your light, you split into two parts, and then you modulate each of these arms. You modulate them out of phase with an RFC tone that's out of phase. And so now you generate side bands on both and now because they're modulating out of phase when they are recombined and on the output splitter and this mock sender interferometer you end up dropping one of the side bands and then the pump and you end up with a shifted side pan. So that's possible you can do single side band modulation with an electronic device but the caveat is that this is now fundamentally lossy. So, you know, you have generated, this other side band via modulation, and the sideband is simply being lost due to interference. So it's their, It's getting combined, it's getting scattered away because there's no mode that it can get connected to. So actually you know, this is going kind of an efficiency less than 3DB usually much less than 3DB. And that's fine if you just have one of these single sideband modulators because you can always amplify, you can send more power but if you're talking about a system and you have many of these and you can't put amplifiers everywhere then, or you're working with quantum information where loss is particularly bad. This is not an option. Now, when you use resonators, you have another option. So here's a device that tries to demonstrate this. This is two resonators that are brought into the near-field of each other. So they're coupled with each other over here where they're, which causes a splitting. And now when we tune the DC voltage was tuned one of these resonators by sort of changing the effective half lengths And one of these resonators tunes, the frequency, we can see an We should see an anti crossing between the two modes and at the center of this splitting this is versus voltage, a splitting at the center at this voltage, let's say here it's around 15 volts. We can see two residences two dips, when we probed the line field going through. And now if we send in the pump resonant, with one of these, and we modulate at this difference frequency we generate this red side band but we actually don't generate the blue side band because there's no optical density of state. So the, so because there's this other side may has just not generated. This system is now much more efficient. In fact, so in Marco Loncar has give they've demonstrated. You can get a hundred percent conversion. And we've also demonstrated this in a similar experiment showing that you can get very large sideband suppression. So, you know more than 30 DB suppression of the side bands with respect to the sideband that you care about It's also interesting that these interactions now preserve quantum coherence. And this is one path to creating links between superconducting microwave systems and optical components. Because now the microwave signal that's scattered here preserves its coherence. So we've also been able to do acoustic optic interactions at these high frequencies. This is a, this is an acoustic optic modulator that operates at a few gigahertz. Basically you generate electric field here which generates a propagating wave inside this transducer made out of lithium niobate. These are aluminum electrodes on top. The phonons are focused down into a small phononic waveguides that guides mechanical waves. And then these are brought into this crystal area where the sound and the Mo and the light are both convert confined to wavelength skill mode volume and they interact very strongly with each other. And the strong interaction leads to very efficient, effective electro-optic modulation. So here we've been able to see, with just a few microwatts of power, many, many side bands being generated. So this is a fact that they like tropic much later where the VPI is, a few thousands of a volt instead of, you know, several volts, which is sort of the off the shelf, electro-optic modulator that you would find. And importantly, we've been able to combine these, photonic and phononic circuits into the same platform. So this is a lithium niobate on same Lithium niobate on Sapphire platform. This is an acoustic transducer that generates mechanical waves that propagate in this lithium niobate waveguide. You can see them here and we can make phononic circuits now. so this is a ring resonate. It's a ring resonator for phonons. So we send sound waves through. And when it's resonance, when its frequency hits the ring residences, we see peaks. and this is, this is cheeks in the drop port coming out. And what's really nice about this platform is that we actually don't need to unlike unlike many memes platforms where you have to have released steps that are usually not compatible with, you know other devices here, there's no release steps. So the phonons are guided in that thin lithium niobate layer. The high Q of these mechanical modes shows that these mechanical resonances can be very coherent oscillators. And so we've also worked towards integrating these with very non-linear microwave circuits to create strongly interacting phonons and phonon circuits. So this is a example of an experiment we did over a year ago, where we have sort of a superconducting Qubit circuit with mechanical resonances made out of lithium niobate shunting the Qubit capacitor to ground. So now vibrations of this mechanical oscillator generate a voltage across these electrodes that couples to the Qubits voltage. And so now you have an interaction between this qubit and the mechanical oscillator, and we can see that in the spectrum of the qubit as we tune it across the frequency band. And we see splittings every time the qubit frequency approaches the mechanical resonance frequency. And infact this coupling is so large, that we were able to observe for the first time, the phonon spectrum. So we can detune this qubit away from the mechanical resonance. And now you have a dispersive shift on the qubit which is proportional to the number of phonons. And because number of photons is quantized. We can actually see, the different phonon levels in the qubit spectrum. Moving forward, we've been trying to, also understand what the sources of loss are in the system. And we've been able to do this by demonstrating by fabricating very large rays in these mechanical oscillators and looking at things like, their quality factor versus frequency. This is an example of a measurement that shows a jump in the quality factor when we enter the frequency band where we expect our phononic band gap for this period, periodic material is this jump you know, in principle,if loss were only due to clamping only due to acoustic waves leaking out in these out of these ends, then this change in quality factor quality factor should go to essentially infinite or should be ex exponential losses should be exponentially suppress with the length So these, but it's not. And that means we're actually limited by other loss channels. And we've been able to determine that these are two level systems and the lithium niobate by looking at the temperature dependence of these losses and seeing that they fit very well sort of standard models that exist for the effects of two level systems on microwave and mechanical resonances. We've also started experimenting with different materials. In fact, we've been able to see that, for example, going to lithium niobate, that's dope with magnesium oxide changes or reduces significantly the effect of the two level systems. And this is a really exciting direction of research that we're pursuing. So we're understanding these materials. So with that, I'd like to thank the sponsors. NTTResearch, of course, a lot of this work was funded by DARPA, ONR, RAO, DOE very generous funding from David and Lucile Packard foundation and others that are shown here. So thank you.

Hi, I'm Hideo Mabuchi from Stanford University. This is my presentation on coherent nonlinear dynamics, and combinatorial optimization. This is going to be a talk, to introduce an approach, we are taking to the analysis, of the performance of Coherent Ising Machines. So let me start with a brief introduction, to ising optimization. The ising model, represents a set of interacting magnetic moments or spins, with total energy given by the expression, shown at the bottom left of the slide. Here the cigna variables are meant to take binary values. The matrix element jij, represents the interaction, strength and sign, between any pair of spins ij, and hi represents a possible local magnetic field, acting on each thing. The ising ground state problem, is defined in an assignment of binary spin values, that achieves the lowest possible value of total energy. And an instance of the easing problem, is specified by given numerical values, for the matrix j and vector h, although the ising model originates in physics, we understand the ground state problem, to correspond to what would be called, quadratic binary optimization, in the field of operations research. And in fact, in terms of computational complexity theory, it can be established that the, ising ground state problem is NP complete. Qualitatively speaking, this makes the ising problem, a representative sort of hard optimization problem, for which it is expected, that the runtime required by any computational algorithm, to find exact solutions, should asyntonically scale, exponentially with the number of spins, and four worst case instances at each end. Of course, there's no reason to believe that, the problem instances that actually arise, in practical optimization scenarios, are going to be worst case instances. And it's also not generally the case, in practical optimization scenarios, that we demand absolute optimum solutions. Usually we're more interested in, just getting the best solution we can, within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for computation. This focus is great interest on, so-called heuristic algorithms, for the ising problem and other NP complete problems, which generally get very good, but not guaranteed optimum solutions, and run much faster than algorithms, that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem, for which extensive compilations, of benchmarking data may be found online. A recent study found that, the best known TSP solver required median runtimes, across a library of problem instances, that scaled as a very steep route exponential, for an up to approximately 4,500. This gives some indication of the change, in runtime scaling for generic, as opposed to worst case problem instances. Some of the instances considered in this study, were taken from a public library of TSPs, derived from real world VLSI design data. This VLSI TSP library, includes instances within ranging from 131 to 744,710, instances from this library within between 6,880 and 13,584, were first solved just a few years ago, in 2017 requiring days of runtime, and a 48 core two gigahertz cluster, all instances with n greater than or equal to 14,233, remain unsolved exactly by any means. Approximate solutions however, have been found by heuristic methods, for all instances in the VLSI TSP library, with, for example, a solution within 0.014% of a known lower bound, having been discovered for an instance, with n equal 19,289, requiring approximately two days of runtime, on a single quarter at 2.4 gigahertz. Now, if we simple-minded the extrapolate, the route exponential scaling, from the study yet to n equal 4,500, we might expect that an exact solver, would require something more like a year of runtime, on the 48 core cluster, used for the n equals 13,580 for instance, which shows how much, a very small concession on the quality of the solution, makes it possible to tackle much larger instances, with much lower costs, at the extreme end, the largest TSP ever solved exactly has n equal 85,900. This is an instance derived from 1980s VLSI design, and this required 136 CPU years of computation, normalized to a single core, 2.4 gigahertz. But the 20 fold larger, so-called world TSP benchmark instance, with n equals 1,904,711, has been solved approximately, with an optimality gap bounded below 0.0474%. Coming back to the general practical concerns, of applied optimization. We may note that a recent meta study, analyze the performance of no fewer than, 37 heuristic algorithms for MaxCut, and quadratic binary optimization problems. And find the performance... Sorry, and found that a different heuristics, work best for different problem instances, selected from a large scale heterogeneous test bed, with some evidence, the cryptic structure, in terms of what types of problem instances, were best solved by any given heuristic. Indeed, there are reasons to believe, that these results for MaxCut, and quadratic binary optimization, reflect to general principle, of a performance complementarity, among heuristic optimization algorithms, and the practice of solving hard optimization problems. There thus arises the critical pre processing issue, of trying to guess, which of a number of available, good heuristic algorithms should be chosen, to tackle a given problem instance. Assuming that any one of them, would incur high cost to run, on a large problem of incidents, making an astute choice of heuristic, is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight, about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This is certainly pinpointed by researchers in the field, as a circumstance and must be addressed. So adding this all up, we see that a critical frontier, for cutting edge academic research involves, both the development of novel heuristic algorithms, that deliver better performance with lower costs, on classes of problem instances, that are underserved by existing approaches, as well as fundamental research, to provide deep conceptual insight, into what makes a given problem instance, easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law, and speculate about a so-called second quantum revolution, it's natural to talk not only about novel algorithms, for conventional CPUs, but also about highly customized, special purpose hardware architectures, on which we may run entirely unconventional algorithms, for common tutorial optimizations, such as ising problem. So against that backdrop, I'd like to use my remaining time, to introduce our work on, analysis of coherent using machine architectures, and associated optimization algorithms. Ising machines in general, are a novel class of information processing architectures, for solving combinatorial optimization problems, by embedding them in the dynamics, of analog, physical, or a cyber-physical systems. In contrast to both more traditional engineering approaches, that build ising machines using conventional electronics, and more radical proposals, that would require large scale quantum entanglement the emerging paradigm of coherent ising machines, leverages coherent nominal dynamics, in photonic or optical electronic platforms, to enable near term construction, of large scale prototypes, that leverage posting as information dynamics. The general structure of current of current CIM systems, as shown in the figure on the right, the role of the easing spins, is played by a train of optical pulses, circulating around a fiber optical storage ring, that beam splitter inserted in the ring, is used to periodically sample, the amplitude of every optical pulse. And the measurement results, are continually read into an FPGA, which uses then to compute perturbations, to be applied to each pulse, by a synchronized optical injections. These perturbations are engineered to implement, the spin-spin coupling and local magnetic field terms, of the ising hamiltonian, corresponding to a linear part of the CIM dynamics. Asynchronously pumped parametric amplifier, denoted here as PPL and wave guide, adds a crucial nonlinear component, to the CIM dynamics as well. And the basic CIM algorithm, the pump power starts very low, and is gradually increased, at low pump powers, the amplitudes of the easing spin pulses, behave as continuous complex variables, whose real parts which can be positive or negative, by the role of soft or perhaps mean field spins. Once the pump power crosses the threshold, for perimetric self oscillation in the optical fiber ring, however, the amplitudes of the easing spin pulses, become effectively quantized into binary values, while the pump power is being ramped up, the FPGA subsystem continuously applies, its measurement based feedback implementation, of the using hamiltonian terms. The interplay of the linearized easing dynamics, implemented by the FPGA , and the thresholds quantization dynamics, provided by the sink pumped parametric amplifier, result in a final state, of the optical plus amplitudes, at the end of the pump ramp, that can be read as a binary strain, giving a proposed solution, of the ising ground state problem. This method of solving ising problems, seems quite different from a conventional algorithm, that runs entirely on a digital computer. As a crucial aspect, of the computation is performed physically, by the analog continuous coherent nonlinear dynamics, of the optical degrees of freedom, in our efforts to analyze CA and performance. We have therefore turn to dynamical systems theory. Namely a study of bifurcations, the evolution of critical points, and typologies of heteroclitic orbits, and basins of attraction. We conjecture that such analysis, can provide fundamental insight, into what makes certain optimization instances, hard or easy for coherent ising machines, and hope that our approach, can lead to both improvements of the course CIM algorithm, and the pre processing rubric, for rapidly assessing the CIM insuibility of the instances. To provide a bit of intuition about how this all works. It may help to consider the threshold dynamics, of just one or two optical parametric oscillators, in the CIM architecture just described. We can think of each of the pulse time slots, circulating around the fiber ring, as are presenting an independent OPO. We can think of a single OPO degree of freedom, as a single resonant optical mode, that experiences linear dissipation, due to coupling loss, and gain in a pump near crystal, as shown in the diagram on the upper left of the slide, as the pump power is increased from zero. As in the CIM algorithm, the non-linear gain is initially too low, to overcome linear dissipation. And the OPO field remains in a near vacuum state, at a critical threshold value, gain equals dissipation, and the OPO undergoes a sort of lasing transition. And the steady States of the OPO, above this threshold are essentially coherent States. There are actually two possible values, of the OPO coherent amplitude, and any given above threshold pump power, which are equal in magnitude, but opposite in phase, when the OPO cross this threshold, it basically chooses one of the two possible phases, randomly, resulting in the generation, of a single bit of information. If we consider two uncoupled OPOs, as shown in the upper right diagram, pumped at exactly the same power at all times, then as the pump power is increased through threshold, each OPO will independently choose a phase, and thus two random bits are generated, for any number of uncoupled OPOs, the threshold power per OPOs is unchanged, from the single OPO case. Now, however, consider a scenario, in which the two appeals are coupled to each other, by a mutual injection of their out coupled fields, as shown in the diagram on the lower right. One can imagine that, depending on the sign of the coupling parameter alpha, when one OPO is lasing, it will inject a perturbation into the other, that may interfere either constructively or destructively, with the field that it is trying to generate, via its own lasing process. As a result, when can easily show that for alpha positive, there's an effective ferromagnetic coupling, between the two OPO fields, and their collective oscillation threshold, is lowered from that of the independent OPO case, but only for the two collective oscillation modes, in which the two OPO phases are the same. For alpha negative, the collective oscillation threshold, is lowered only for the configurations, in which the OPO phases are opposite. So then looking at how alpha is related to the jij matrix, of the ising spin coupling hamilitonian, it follows the, we could use this simplistic to OPO CIM, to solve the ground state problem, of the ferromagnetic or antiferromagnetic angles, to ising model, simply by increasing the pump power, from zero and observing what phase relation occurs, as the two appeals first start to lase. Clearly we can imagine generalizing the story to larger, and, however, the story doesn't stay as clean and simple, for all larger problem instances. And to find a more complicated example, we only need to go to n equals four, for some choices of jij for n equals four, the story remains simple, like the n equals two case. The figure on the upper left of this slide, shows the energy of various critical points, for a non frustrated n equals for instance, in which the first bifurcated critical point, that is the one that, by forgets of the lowest pump value a, this first bifurcated critical point, flows asyntonically into the lowest energy using solution, and the figure on the upper right, however, the first bifurcated critical point, flows to a very good, but suboptimal minimum at large pump power. The global minimum is actually given, by a distinct critical point. The first appears at a higher pump power, and is not needed radically connected to the origin. The basic CIM algorithm, is this not able to find this global minimum, such non-ideal behavior, seems to become more common at margin end, for the n equals 20 instance show in the lower plots, where the lower right pod is just a zoom into, a region of the lower left block. It can be seen that the global minimum, corresponds to a critical point, that first appears that of pump parameter a around 0.16, at some distance from the adriatic trajectory of the origin. That's curious to note that, in both of these small and examples, however, the critical point corresponding to the global minimum, appears relatively close, to the adiabatic trajectory of the origin, as compared to the most of the other, local minimum that appear. We're currently working to characterise, the face portrait typology, between the global minimum, and the adiabatic trajectory of the origin, taking clues as to how the basic CIM algorithm, could be generalized, to search for non-adiabatic trajectories, that jumped to the global minimum, during the pump up, of course, n equals 20 is still too small, to be of interest for practical optimization applications. But the advantage of beginning, with the study of small instances, is that we're able to reliably to determine, their global minima, and to see how they relate to the idea, that trajectory of the origin, and the basic CIM algorithm. And the small land limit, We can also analyze, for the quantum mechanical models of CAM dynamics, but that's a topic for future talks. Existing large-scale prototypes, are pushing into the range of, n equals, 10 to the four, 10 to the five, 10 to the six. So our ultimate objective in theoretical analysis, really has to be, to try to say something about CAM dynamics, and regime of much larger in. Our initial approach to characterizing CAM behavior, in the large end regime, relies on the use of random matrix theory. And this connects to prior research on spin classes, SK models, and the tap equations, et cetera, at present we're focusing on, statistical characterization, of the CIM gradient descent landscape, including the evolution of critical points, And their value spectra, as the pump powers gradually increase. We're investigating, for example, whether there could be some way, to explain differences in the relative stability, of the global minimum versus other local minima. We're also working to understand the deleterious, or potentially beneficial effects, of non-ideologies such as asymmetry, in the implemented using couplings, looking one step ahead, we plan to move next into the direction, of considering more realistic classes of problem instances, such as quadratic binary optimization with constraints. So in closing I should acknowledge, people who did the hard work, on these things that I've shown. So my group, including graduate students, Edwin Ng, Daniel Wennberg, Ryatatsu Yanagimoto, and Atsushi Yamamura have been working, in close collaboration with, Surya Ganguli, Marty Fejer and Amir Safavi-Naeini. All of us within the department of applied physics, at Stanford university and also in collaboration with Yoshihisa Yamamoto, over at NTT-PHI research labs. And I should acknowledge funding support, from the NSF by the Coherent Ising Machines, expedition in computing, also from NTT-PHI research labs, army research office, and ExxonMobil. That's it. Thanks very much.

yeah I probably would use a sort of ever-changing I would say ever-expanding you know but you have to write because what we saw when we started off is roll around how to automate my datacenter how do I get a cloud experience in my data center what we see changing and okay Frank is driven by this whole app refactoring process that customers want to deploy apps maybe in the cloud maybe develop in the cloud and so they need an extension to the automated data center into the cloud and so really what you see from us is an expansion of that ACA concept you rangas point we actually really didn't change we just we're just extending it to container development platforms two different cloud environments what's the same area automate end-to-end network reach as well as the segmentation what is the right there right sorry security regime in this you know cloud era how is it evolving well I mean what we're doing is we're bringing tools like tetration which now runs on Prem and in the cloud things like stealthWatch which runs on from in the cloud and simply bringing them security frameworks that are very effective we're I think a very capable of well known security vendor but bringing them the capability to run the same capabilities in their on-prem environments and their data centers as well as in multiple public clouds and that just eliminates the seams that hackers could maybe get into it makes common policy Possible's they can define policy around an application once and have that apply across the vault environments which not only it's easier for them but it eliminates potential mistakes that they might make that might leave things open to a hacker so for us it's that simple bringing very effective common frameworks for security across all these cisco has embraced the idea of being a platform and not a siloed individual product line and so for a service provider like CenturyLink for us to be able to embrace that same philosophy of the platform of services what that means is that our engineering and field ops folks our Operations teams do all the hard work on the back end to make sure that we have established all of the right security the right network the reliability the global scalability of our specific platform of services and being that leader in telecommunications and then we're able to lay that cisco platform on top of it and what happens then from a product management level is once you've established that foundation it's really plug-and-play the customer calls and says I need calling I need meetings I need you know whatever it is they need and we build that solution and very quickly can put those components into play and get them to use the service right away so what we've done across the portfolio even in primary storage is made sure that we've done all sorts of things that help you against a ransomware a malware attack keep the data encrypted I think the key point and actually I think Silicon angle wrote about this is like some like 98% of all enterprises getting a broke it in two anyway so it's great that you've got security software on the edge with at the IBM or RSA or blue coat or checkpoint oh who cares who you buy the software from but when they're in there stealing and sometimes you know some accounts have told us they can track them down in a day but if you're a giant global fortune 500 datacenter look it may take you like a week so they can be stealing stuff right and left so we've done everything from we have right once technology right so it's immutable data you can't change it we've got encryption so if they steal it guess what they can't use it but the other thing we've done is real protection against ransomware now that's a great question in terms of modernization of infrastructure and there's some really interesting trends that I think are occurring and I think the one that's getting a lot of us is really edge computing and what we're finding is depending on the use case it can be an enterprise application where you're trying to get localization of your data it could be an IOT application where it's it's really critical for latency or bandwidth to keep compute and data close to the thing if you will or it could be mobile edge computing where you want to do thing like analytics and AI on a video stream before you tax the the bandwidth of the cellular infrastructure with that data stream so across the board I think edge is super exciting and you can't talk about edge with like I said talking about artificial intelligence another big trend whether it's running native running with an accelerator an FPGA I think we're seeing a myriad of use cases in that space but Security's in the end to your point right I've got software to find access I've got mobile access points I've got you know tetration I've got you know all of these products that are helping people that in the past they were just patching holes in the dike you know hey this happened let's put this software product here this happened let's put this in and we actually built the security practice like the last three or four years ago it's growing you know the number of people that are whether it's regulation compliance you know I got some real problem I think I've got a problem and I don't know what it is our ability to come back and sit down and say let's evaluate what your situation is so I was talking to the networking guys and so Wow enterprise networking it's up way up what's driving that the need to transform or is that you know what is it they're like a lot of times it's something are long security that's making them step back and reevaluate and then sometimes that transfer translates into an entire network refresh there are tools that people use and everybody's environments a little different so some might want to integrate in and use ansible terraform you know tools like that and so then you need code that will help integrate into that other people are using ServiceNow for tickets so if something happens integrate into that people are using different types of devices hopefully mostly Cisco but they may be other using others as well we can extend code that goes into that so it really helps to go in different areas and what's kind of cool is that our there's an amount of code that where people have the same problems you know and you know you start doing something everyone has to make the first few kind of same things in software let's get that into exchange and so let's share that there's places where partners are gonna want to differentiate keep that to yourselves like use that as your differentiated offer and then there's areas where people want to solve in communities of interest so we have we have someone who does networking and he wants to do automation he does it for power management in the utilities industry so he wants a community that will help write code that'll help for that area you know so people have different interests and you know we're hoping to help facilitate that because Cisco actually has a great community we have a great community that we've been building over the last 30 years there the network experts they're solving the real problems around the world they work for partners they work for customers and we're hoping that this will be a tool to get them to band together and contribute in a in a software kinda way they have the right reason to be afraid because so many automation was created a once user exactly was right and then you have the cost of traditional automation you have the complexity to create a network automation you guys realize that middle coordination you cannot have little automation only work on a portion of your needle you have to work on majority if not all of your needle right so that's became very complex just like a you wanna a self-driving car you can go buy a Tesla a new car you can drive on its own but if you wanna your 10 year order Toyota driving on its own richer feared that's a very complex well let's today Network automation how to deal with it you have to deal with multi vendor technology Marty years of technology so people spend a lot of money the return are very small they so they have a right to affair afraid of it but the challenge is there is what's alternative yeah I think that is one of the things that's very unique about the definite community is within the community we have technical stakeholders from small startups to really large partners or huge enterprises and when we're all here in the demo soon we're all engineers and we're all exchanging ideas kind of no matter what the scale so it becomes this great mixing of you know shared experiences and ideas and that is some of the most interesting conversations that I've actually heard this week is people talking about how maybe they're using one Cisco platform in these two very different environments and exchanging ideas about how they do that or maybe how they're using a Cisco platform with an open-source tool and then people finding value in thinking oh maybe I can do that in my environment so that part of the ecosystem and community is very interesting and then we're also helping partners find each other so we do a lot of work around you know here's a partner in the Cisco ecosystem who goes and installs Meraki networks right here's a software partner who builds mapping technology on top of indoor Wi-Fi networks and getting those two together because the software partner is not going to install the network and the network person may not write that application in that way and so bringing them together we've had a lot of really good information coming back from the community around kind of finding each other and being able to deliver those outcomes what are you guys doing Tom we'll start with you how are you guys working together to infuse and integrate security into the technologies and that from a customer's perspective those risks that dial down yeah so so we're in Cisco's integrating security across all of our product portfolio right and and that includes our data center portfolio all the way through our campus our when all those portfolios so we continue to look for opportunities to to integrate you know whether it's dual factor authentication or things like secure data center with a fire you know of highly scalable multi instance firewall in front of a data center things like that so we're we're definitely looking for areas and angles and opportunities for us to not only integrate it from a product standpoint but also ensure that we are talking that story with our customers so that they know they can they can leverage Cisco for the full architecture from a security standing on the storage of the data from an encryption perspective and as it gets moved or his mobile you know that that level of security and policy follows it you know wherever the data is secure of course enemy everybody always wants more performance they want lower cost security in many ways has begun to trump those other two attributes they've they've become table stakes security as well but security is really number one now ya talk about that talk about the major trends that you're seeing well of course of course security now is top of mine for everyone board level conversations executive level conversations all the time I think what ends up happening is in the past we would think about it as Network performance cost etc security as a tangent kind of side conversation now of course it's built into everything that we do [Music]

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Welcome back Inside the Sands. Here's to continue our coverage here. Live on the Cube of AWS Reinvent 2019 Absolutely jam packed isles. Great educational sessions and one of the feature presenters now joins us well. Dave Alana John Walls with Paul Saville. Who's the SPP of court networking technology solutions at Caen. Freely. Paul, Good to see you again. >>Yeah, let's see you, John. >>So you just finished up. We'll get in that just a little bit. First off, just give me your impression of what's going on here and the energy and the vibe that you're getting. >>Yeah, I think it's fantastic. I mean, it's very high energy here, you know, there's a lot of new things that that are emerging terms of the applications that we're seeing the use cases for the cloud. And of course, exciting stuff happened around ej compute with the announcement of AWS with the outpost, Long >>will jump in Najaf. Everybody has a different idea, right? You weren't so I mean, if you define the edge, at least. How do you see it? >>Yeah, it's very simple definition of how we see the edge. It's putting compute very close to the point of interaction, and the interaction could be with humans or the inner action could be with devices or other electron ICS that need toe that need to be controlled or that need to communicate. But the point is getting that that computers close as possible to it from a performance standpoint that's needed. >>Okay, so we heard that a lot from Andy Jassy ethic yesterday. Right now compute to the data. I mean, with all due respect, it's like he was talking about like it was a new concept, right? We've been here for quite some time, so talk more about how you see the edge evolving. I mean, look, I have a lot of credit to Amazon because, you know, they used to not talk about hybrid. I predict a couple years to talk about multi cloud. Guarantee it because that's what customers are doing, so they respond to customers at the same time. I like their edge strategy because it's all about developers. Infrastructures code on the edge But you guys are about, you know, moving that data on or not necessarily bring in the computer that. So how do you see the edge >>evolving? Yeah, so the reason this whole trend is happening is because what's happening with the new technologies that are enabling a whole new set of applications out there? Things like What's going on with artificial intelligence and machine learning and virtual reality those the robotics control Those things are basically driving this need to place compute as close as possible to that point of interaction. The problem is that when you do that, costs go up. And that's the conundrum that we've kind of been in because when Compute gets housed at the customer premise in a home in a business in an enterprise, then that's the most expensive real estate that that there is, and you can't get the economies of scale that's there. The only other choice to date has been the public cloud, and that could be hundreds or thousands of miles away. And these new applications that require really tight control and interaction can't operate in that kind of environment, And yet it's too expensive to run those applications at the very edge at the premise itself. So that's why this middle ground now of a place and compute nearby, where conserve many locations or must be house more cost effectively. >>Okay, so you got the speed of light problem, right? So you deal with that later by making the compute proximate to the data, but it doesn't have to be like right next to it. Correct. But But what are we talking distance wise? It's that to be synchronised distance or >>when we think of the distance, we think about it in terms of milliseconds of delay, from where the edge device, the thing that needs to interact with the computer, the application needs to interact with. And we have not seen any applications that from the customers we talked to that really get beyond our need tighter than five milliseconds of delay. Now that's one way. So if we get into that range of place and compute within five milliseconds of the of the edge interaction, the device that it needs to interact with, that is enough to meet some of the most tightest requirements that we've seen around robotics control, video analytics and another >>like I could ship code to the data. But the problem is, if it needs to be real time, right, it's still too much. It's too much late, right? That's the problem that you're solving. That's right. Okay, >>so what's what you were talking about? Why milliseconds matter? That's right. So give me some examples, if you will, then about why, why five matters more than 10 or five matters more than eight or 20 or whatever, because we're talking about such an infant testable difference. But yet it does matter. In some respects. It does, >>because so give you an example of robotics, for example, robotics control. You know that is one of things that requires the most tight Leighton see requirement because it depends upon the robotics itself. If it's a machining tools that's working on a laid, then that doesn't require a tide of response time to the controller as, say, a scanning device that Israel time pushing things around very fast in doing an optical read on it to make the decision about how about where it pushes the device next, that type of interaction of control requires a much tighter, late and see performance, and that's why you get start, you start to see these ranges. But as I said, we're not seeing anything below that kind of five millisecond type of range from >>the other thing that's changing it and help me understand. This is yeah, Okay, you're moving the compute closer to the data, which increases costs. And I want to understand how you're addressing that. Maybe one of the ways addresses you're bringing the cloud model, the operating model to the data. So right patches, security patches, maintenance, things like that are reduced. Is that how you're addressing costs? >>Yeah, that is part of it. And that's why the eight of US outpost is very interesting because it is really a complete instance of AWS that is in a much smaller form factor that you can deploy very close to that point of interaction close to the customer to the customer premise, and that enables customers to leverage pretty much the full power of AWS in engaging with those devices and coding to those devices and dropping those applications closed. >>Now you lose the multi tenant aspect Is that right down unnecessarily >>from our understanding of outpost, it's a single 10 a device coming out the gate. But ultimately it's gonna be a multi tenant device. >>Yeah, okay, so near term, it's easier to manage. But it's it's multi instance, I guess, yeah, over time, maybe you could share that. That resource is still not getting. >>The interesting thing is that even though it's a single tenant device, there's still many great use cases because even a single Tenet device in set in one market could serve multiple enterprise locations. So it still has that kind of a sense of scale because you concert as long as it's it's one enterprise. Conserve many locations off of that one. That one device. >>Okay, so you don't get the massive economies of scale, but you're opening abuse cases that never existed before. >>That's right. But what about what do you do with the data supplied basically held something data scale and edge devices creating that much more data. All of a sudden speed becomes a little more challenging, taking in a lot more information, trying to process in different ways after feeding off of that, so a sudden you have a much more complex challenge because it's not static, right? This is a very dynamic environment, >>That's right. Yeah, and there's a very big trend that's happening now, which is that data is being created at the edge, and it's staying at the edge for a whole number of reasons. You know, in the Old World you would pretty much collect data and you'd ship it off to the centralized data center or to the public cloud to be housed there. And that's today. That's where 80% of data resides. But there's a big shift happening where that data now needs to reside at the deep edge because it needs to have that fast interaction with something that's that's working with or because of government regulations that are now coming in that are having much stricter tolerances around. You have to know exactly where your data is can't cross state lines. It can't, you know, get out of certain security zone. Things like that are forcing companies now to keep that massive amount of data in a very understand known localized position. >>You gotta act on it in real time. Yeah, some of it will go back to the cloud, but you see folks persist. The data at the edge or not so much persistent data. People want to store it at the edges. Well, >>uh, people in the story at the edge where where it's going to have a lot of interaction. So if you're running A if you're running a chemical plant, you may not need to have access to a lot of data outside that chemical plant. But you you're intensively analyzing that data in the chemical plant, and you don't want to ship it off someplace centrally, 1000 miles away. To be access from there. It needs to be acted on locally, and that's why it's compute this movement toward EJ computers really building and becoming stronger. >>Talk about your tech. You know what? What's the real value of what you do? You obviously reducing late, sees they gotta secure all this stuff but >>central and brings the number of tools to help in this whole space. So the first of all, the network that we provide that could tie it all together from the enterprise location to the to the edge location where compute can be housed all the way back to the public cloud core way have a network that spans the entire U. S. Fiber all over the place, and we can use those lonely and see fiber optic connections to change those those areas together in the most optimal fashion. To get the kind of performance that you need to handle these distributed computing environments, we also bring compute technology itself. We have our own variety of EJ compute, where we can build custom edge compute solutions for customers that meet their very specific SPECT requirements that could be dedicated to them. We can incorporate AWS computer technology as well, and we have way have I t service's and skilled people, thousands of employees that are focused on the space that build these solutions together. For customers that tie together, the public cloud resource is the edge. Compute resource is the network resource is the wireless connectivity capabilities that's needed on customer premise and the management solutions to tie it all together in that very mixed environment. >>We were just on a session with Teresa Carlson runs public sector for AWS, telling the SAT in a session. Marty Walsh, the mayor of Boston, has got this big smart city initiative going on. I know that's one of the cases you're working on. Maybe talk about that a little bit. And maybe some of the other interesting use cases. >>Yeah, that's right. Definitely. Smart cities are a big our big use case, though. The one and we're we're actually actively working on a number of them. I would say that those used the smart City use cases tend to move very slowly because you're talking about municipalities and long decision making cycle, I'll tell you that. We've seen >>there's a 50 year plan he put forward, >>but the use cases that we're really seeing the most traction with our interestingly is robotics is a really big one, and Video Analytics is another big one. So we're actually deploying edge used case solutions right now. In those scenarios, the Robotics one is a great one because those devices need to be. Those robotic devices need to be controlled within a really tight millisecond tolerance, and but the computer needs to be housed in a very it's much more reliable economic location. The video Analytics piece is a really interesting one that we're seeing very, very big demand for, because retailers have now reached the point with the technology where they can do things like they can, they can figure out by doing video analytics whether somebody is acting suspiciously in the store and we're hearing that they can, they think they can now cut Devery out of retail locations dramatically by using video analytics. And when you talk about big savings to the bottom line of a company that makes a big savings to them so that those very to good use cases we're seeing that a real today. You >>know what the other things you were talking about earlier was about the disappearance of Compute Divide. So where to go? Wait. >>I like to say that in the old days, if you've been around long enough like I know you're old because watching you on TV >>way get out of college, Does that make you feel way get out of college? >>Everything was in the mainframe, right? You essentially. Yet when you went to work, you had a terminal, and everything was house Essentially. Then we went to distributed where client server model, where you everybody was working on desktops and a lot of the compute was on the desk tops and very little went back to a mainframe. Then we made the ship to the cloud where he pushed his much in the centralized location as we can, too. So he's shifted way back to centralized. That's the compute divide. I'm talking about goat, that big ship from decentralized, centralized, decentralized. Now we're actually moving to a new world where that pendulum swing that compute divide is disappearing because compute isn't most economically stored. Anyone location, it's everywhere. It's gonna be at the Io ti edge. It's gonna be at the premise it's going to be in market locations. They were essential. Eyes is gonna be in the public cloud core. It's gonna be all around us. And that's what I mean by the by the disappearance of the compute >>divine. And, you know, I wantto come back on that. You talk about a pendulum. A lot of people talk about the pendulum swings mainframe and distributed. A lot of people say it's the pendulum is swinging back, but you just described it differently. It's It's a ubiquitous matrix. Now you'd is everywhere. >>That's where you hear the term fog computing the idea of the fog. Now it's not the cloud that you can see off in the distance. It's just everywhere, right, surround you and that's how combines we can start to think about how >>I first heard that you're like, I don't know eight years ago. What the heck is this? It was ahead of its time, but now it's really starting to show. This is sort of new expansion of what we know is cloud reading redefining? Yes, exactly. Net ej five g. That's, you know, another big piece of it. You know, Amazon's obviously excited about that with wavelength, right? What do you see for five G? How's that? It can affect this whole equation. >>Yeah, I think five G is gonna have a have a number of EJ applications and was primarily gonna be around the mobile space. You know, it's the the advantage of it is that it increases band with and support smoke mobility, and it allows for a little bit higher resilience because they can take the part of the spectrum and make sure that they're carving it out and dedicating it for particular applications that are there. But I tell you that the five G gets a lot of attention in terms of being how EJ computer's gonna roll out. But we're not saying that at all. edge compute is available today and that we're providing those edge compute solutions through our fiber optic networks. What we're seeing is that every enterprise that we're talking to once fiber into their into their enterprise location. Because once you have fiber there, that's gonna be the most secure, reliable and scalable solutions fiber kin can effectively scale as Bigas. Any customer could ever consume the bandwidth. And they know that once they get fiber into that application into their location that they're good for for the future because they can totally scale with that. And that's how we're deploying edge solutions today, >>Paul. I know you got a plane to catch, and you got to go. But after that age comment, we're gonna keep you for another hour. No, I think it's great. You're doing all right. All right, Hang on. We're about to say goodbye to Paul now. Well, you have a free event. 2019. Coverage continues. Right here on the right

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Welcome back to the Cube. Here live in Las Vegas for aws reinvent I'm John for a devil on the ads, always extracting the signal from the noise. We're here for 1/7 reinvent of the eight years that they've had at what a wave. One of the biggest waves is the modernization of procurement, the modernization of business, commercial business and the rapid acceleration of public sector. We're here with the chief of public sector for AWS. Teresa Carlson, vice president publics that globally great to have you >>so great to have the Q begin this year. We appreciate you being here, >>so we're just seeing so much acceleration of modernization. Even in the commercial side, 80 talks about transformation. It's just a hard core on the public sector side. You have so many different areas transforming faster because they haven't transformed before. That's correct. This is a lot of change. What's changed the most for you in your business? >>Well, again, I'll be here 10 years this mad that A B s and my eighth reinvent, and what really changed, which was very exciting this year, is on Monday. We had 550 international government executives here from 40 countries who were talking about their modernization efforts at every level. Now again, think about that. 40 different governments, 550 executives. We had a fantastic day for them planned. It was really phenomenal because the way that these international governments or think about their budget, how much are they going to use that for maintaining? And they want to get that lesson last. Beckett for Modernization The Thin John It's a Beckett for innovation so that they continue not only modernized, but they're really looking at innovation cycles. So that's a big one. And then you heard from somewhere customers at the breakfast this morning morning from from a T. F. As part of the Department of Justice. What they're doing out. I'll call to back on firearms. They completely made you the cloud. They got rid of 20 years of technical debt thio the Veterans Administration on what they're digging for V A benefits to educational institutions like our mighty >>nose, and he had on stages Kino, Cerner, which the health care companies and what struck me about that? I think it relates to your because I want to get your reaction is that the health care is such an acute example that everyone can relate to rising costs. So cloud helping reduce costs increase the efficiencies and patient care is a triple win. The same thing happens in public sector. There's no place to hide anymore. You have a bona fide efficiencies that could come right out of the gate with cloud plus innovation. And it's happening in all the sectors within the public sector. >>So true. Well, Cerner is a great example because they won the award at V a Veteran's administration to do the whole entire medical records modernization. So you have a company on stage that's commercial as I met, commercial as they are public sector that are going into these large modernization efforts. And as you sit on these air, not easy. This takes focus and leadership and a real culture change to make these things happen. >>You know, the international expansion is impressive. We saw each other in London. We did the health care drill down at your office is, of course, a national health. And then you guys were in Bahrain, and what I deserve is it's not like these organizations. They're way behind. I mean, especially the ones that it moved to. The clouds are moving really fast. So well, >>they don't have as much technical debt internationally. It's what we see here in the U. S. So, like I was just in Africa and you know what we talked about digitizing paper. Well, there's no technology on that >>end >>there. It's kind of exciting because they can literally start from square one and get going. And there's a really hunger and the need to make that happen. So it's different for every country in terms of where they are in their cloud journey. >>So I want to ask you about some of the big deals. I'll see Jet eyes in the news, and you can't talk about it because it's in protest and little legal issues. But you have a lot of big deals that you've done. You share some color commentary on from the big deals and what it really means. >>Yeah, well, first of all, let me just say with Department of Defense, Jet are no jet. I We have a very significant business, you know, doing work at every part of different defense. Army, Navy, Air Force in the intelligence community who has a mission for d o d terminus a t o N g eight in a row on And we are not slowing down in D. O d. We had, like, 250 people at a breakfast. Are Lantian yesterday giving ideas on what they're doing and sharing best practices around the fence. So we're not slowing down in D. O d. We're really excited. We have amazing partners. They're doing mission work with us. But in terms of some really kind of fend, things have happened. We did a press announcement today with Finn Rat, the financial regulatory authority here in the U. S. That regulates markets at this is the largest financial transactions you'll ever see being processed and run on the cloud. And the program is called Cat Consolidated Audit Trail. And if you remember the flash crash and the markets kind of going crazy from 2000 day in 2008 when it started, Finneran's started on a journey to try to understand why these market events were happening, and now they have once have been called CAT, which will do more than 100 billion market points a day that will be processed on the cloud. And this is what we know of right now, and they'll be looking for indicators of nefarious behavior within the markets. And we'll look for indicators on a continuous basis. Now what? We've talked about it. We don't even know what we don't know yet because we're getting so much data, we're going to start processing and crunching coming out of all kinds of groups that they're working with, that this is an important point even for Finn rep. They're gonna be retiring technical debt that they have. So they roll out Cat. They'll be retiring other systems, like oats and other programs that they >>just say so that flash crash is really important. Consolidated, honest, because the flash crash, we'll chalk it up to a glitch in the system. Translation. We don't really know what happened. Soto have a consolidated auto trail and having the data and the capabilities, I understand it is really, really important for transparency and confidence in the >>huge and by the way, thinner has been working with us since 2014. They're one of our best partners and are prolific users of the cloud. And I will tell you it's important that we have industries like thin red regulatory authorities, that air going in and saying, Look, we couldn't possibly do what we're doing without cloud computing. >>Tell me about the technical debt because I like this conversation is that we talk about in the commercial side and developer kind of thinking. Most businesses start ups, Whatever. What is technical debt meet in public sector? Can you be specific? >>Well, it's years and years of legacy applications that never had any modernization associated with them in public sector. You know now, because you've talked about these procurement, your very best of your very savvy now public sector >>like 1995 >>not for the faint of heart, for sure that when you do procurement over the years when they would do something they wouldn't build in at new innovations or modernizations. So if you think about if you build a data center today a traditional data center, it's outdated. Tomorrow, the same thing with the procurement. By the time that they delivered on those requirements. They were outdated. So technical debt then has been built up years of on years of not modernizing, just kind of maintaining a status quo with no new insides or analytics. You couldn't add any new tooling. So that is where you see agencies like a T F. That has said, Wow, if I'm gonna if I'm gonna have a modern agency that tracks things like forensics understands the machine learning of what's happening in justice and public safety, I need to have the most modern tools. And I can't do that on an outdated system. So that's what we kind of call technical death that just maintains that system without having anything new that you're adding to >>their capabilities lag. Everything's products bad. Okay, great. Thanks for definite. I gotta ask you about something that's near and dear to our heart collaboration. If you look at the big successes in the world and within Amazon Quantum Caltex partnering on the quantum side, you've done a lot of collaboration with Cal Cal Poly for ground station Amazon Educate. You've been very collaborative in your business, and that's a continuing to be a best practice you have now new things like the cloud innovation centers. Talk about that dynamic and how collaboration has become an important part of your business model. >>What we use their own principles from Amazon. We got building things in our plan. Innovation centers. We start out piloting those two to see, Could they work? And it's really a public private partnership between eight MPs and universities, but its universities that really want to do something. And Cal Poly's a great example. Arizona State University A great example. The number one most innovative university in the US for like, four years in a row. And what we do is we go in and we do these public sector challenges. So the collaboration happens. John, between the public sector Entity, university with students and us, and what we bring to the table is technical talent, air technology and our mechanisms and processes, like they're working backwards processes, and they were like, We want you to bring your best and brightest students. Let's bring public sector in the bowl. They bring challenges there, riel that we can take on, and then they can go back and absorb, and they're pretty exciting. I today I talked about we have over 44 today that we've documented were working at Cal Poly. The one in Arizona State University is about smart cities. And then you heard We're announcing new ones. We've got two in France, one in Germany now, one that we're doing on cybersecurity with our mighty in Australia to be sitting bata rain. So you're going to see us Add a lot more of these and we're getting the results out of them. So you know we won't do if we don't like him. But right now we really like these partnerships. >>Results are looking good. What's going on with >>you? All right. And I'll tell you why. That why they're different, where we are taking on riel public sector issues and challenges that are happening, they're not kind of pie in the sky. We might get there because those are good things to do. But what we want to do is let's tackle things that are really homelessness, opioid crisis, human sex trafficking, that we're seeing things that are really in these communities and those air kind of grand. But then we're taking on areas like farming where we talked about Can we get strawberries rotting on the vine out of the field into the market before you lose billions of dollars in California. So it's things like that that were so its challenges that are quick and riel. And the thing about Cloud is you can create an application and solution and test it out very rapidly without high cost of doing that. No technical Dan, >>you mentioned Smart Cities. I just attended a session. Marty Walsh, the mayor of Boston's, got this 50 50 years smart city plan, and it's pretty impressive, but it's a heavy lift. So what do you see going on in smart cities? And you really can't do it without the cloud, which was kind of my big input cloud. Where's the data? What do you say, >>cloud? I O. T is a big part at these. All the centers that Andy talked about yesterday in his keynote and why the five G partnerships are so important. These centers, they're gonna be everywhere, and you don't even know they really exist because they could be everywhere. And if you have the five G capabilities to move those communications really fast and crypt them so you have all the security you need. This is game changing, but I'll give you an example. I'll go back to the kids for a minute at at Arizona State University, they put Io TI centers everywhere. They no traffic patterns. Have any parking slots? Airfield What Utilities of water, if they're trash bins are being filled at number of seats that are being taken up in stadiums. So it's things like that that they're really working to understand. What are the dynamics of their city and traffic flow around that smart city? And then they're adding things on for the students like Alexis skills. Where's all the activity? So you're adding all things like Alexa Abs, which go into a smart city kind of dynamic. We're not shop. Where's the best activities for about books, for about clothes? What's the pizza on sale tonight? So on and then two things like you saw today on Singapore, where they're taking data from all different elements of agencies and presenting that bad to citizen from their child as example Day one of a birth even before, where's all the service is what I do? How do I track these things? How do I navigate my city? to get all those service is the same. One can find this guy things they're not. They're really and they're actually happening. >>Seems like they're instrumented a lot of the components of the city learning from that and then deciding. Okay, where do we double down on where do we place? >>You're making it Every resilient government, a resilient town. I mean, these were the things that citizens can really help take intro Web and have a voice in doing >>threes. I want to say congratulations to your success. I know it's not for the faint of heart in the public sector of these days, a lot of blockers, a lot of politics, a lot of government lockers and the old procurement system technical debt. I mean, Windows 95 is probably still in a bunch of PCs and 50 45 fighters. 15 fighters. Oh, you've got a great job. You've been doing a great job and riding that wave. So congratulations. >>Well, I'll just say it's worth it. It is worth it. We are committed to public sector, and we really want to see everyone from our war fighters. Are citizens have the capabilities they need. So >>you know, you know that we're very passionate this year about going in the 2020 for the Cube and our audience to do a lot more tech for good programming. This'll is something that's near and dear to your heart as well. You have a chance to shape technology. >>Yes, well, today you saw we had a really amazing not for profit on stage with It's called Game Changer. And what we found with not for profits is that technology can be a game changer if they use it because it makes their mission dollars damage further. And they're an amazing father. And send a team that started game changer at. Taylor was in the hospital five years with terminal cancer, and he and his father, through these five years, kind of looked around. Look at all these Children what they need and they started. He is actually still here with us today, and now he's a young adult taking care of other young Children with cancer, using gaming technologies with their partner, twitch and eight MPs and helping analyze and understand what these young affected Children with cancer need, both that personally and academically and the tools he has He's helping really permit office and get back and it's really hard, Warren says. I was happy. My partner, Mike Level, who is my Gran's commercial sales in business, and I ran public Sector Day. We're honored to give them at a small token of our gift from A to B s to help support their efforts. >>Congratulates, We appreciate you coming on the Cube sharing the update on good luck into 2020. Great to see you 10 years at AWS day one. Still, >>it's day one. I feel like I started >>it like still, like 10 o'clock in the morning or like still a day it wasn't like >>I still wake up every day with the jump in my staff and excited about what I'm gonna do. And so I am. You know, I am really excited that we're doing and like Andy and I say we're just scratching the surface. >>You're a fighter. You are charging We love you, Great executive. You're the chief of public. Get a great job. Great, too. Follow you and ride the wave with Amazon and cover. You guys were documenting history. >>Yeah, exactly. We're in happy holidays to you all and help seeing our seventh and 20 >>so much. Okay, Cube coverage here live in Las Vegas. This is the cube coverage. Extracting the signals. Wanna shout out to eight of us? An intel for putting on the two sets without sponsorship, we wouldn't be able to support the mission of the Cube. I want to thank them. And thank you for watching with more after this short break.

>>live from Boston, Massachusetts. It's the Q covering I f s World Conference 2019. Brought to you by I >>f. S, I say, What a minute. I didn't cash it. Everybody welcome to I f s World 2019. You watching the Cube? The leader in live tech coverage on day Volante with my co host, Paul Galen. Marty Martin is here. She is the president of the service management division of I F s and C e o of work wave. Marty, good to see you. >>Yeah, it's great to be here. I'm so excited. >>A lot of action going on. You guys. Service management, Field Service management particular. You guys had an acquisition today. We're gonna talk about Let's start with your role you came in and 2017 with the >>pretty acting. Actually, >>2018 finalized the acquisition. I think they announce it in 2017. So tell us about how you came in and where you're at today with >>Certainly. So work wave the company. I lied. Join the effects family in 2017. Darren Ruess, who joined I f s in April 2018 recruited me into form a global business unit around service in August of 2018 and the reason why we did this is service isn't only a part of our economies all over the world, but it's a super great growth area that almost every business can go after in in progress both revenue and margins. So we had a lot of great software products, and we really wanted to improve our go to market around this. >>So why, why all of a sudden today, this talk about service management? Why's it becoming so hard? I mean, everybody's always been focused on customer service, but why this service management generally and field service management while the buzz. >>So first off, you've had the evolution of a number of line of business applications and service certainly has been a part of maintenance organizations or break fix where you're going out in repairing thing. What we're realizing now when you talk about service ization, how o E EMS air building what's called aftermarket revenue? There is literally $100 billion of revenue that you can get from that you look, we had Melissa did a nano from Souza. If you think about open source software, they make money from sirve ties, ing, open source software and the products. You look at apple how they're doing APs. So people are starting to realize that service is an engine for brand loyalty, customer experience, not just a cost center. How it used to be, what the >>customers do. Ah, companies do wrong with service one of the areas where they tend to have the greatest inefficiencies where you can help him. >>So first off, I'd say that often in the C suite, unless they're pure place service companies. They don't understand how transformative service is and how important it is to their brand. Many times now, if you have digital enablement of a new customer, the first time they see a face of your brand might be your service technician. So getting the awareness of the C suite is Step one, because we want to start talking about outcomes that grow revenue and profits and getting them to invest in service. So you know, many times will say, Oh, I want to do a C. R M project. I want to do an E r P project. That's certainly things were good at it. Here I a fest, but we can coach them through how you take the market opportunity for your company and service enabled by our technology and transform. Tomorrow I'll be with Accenture, one of our many great partners, and we're talking about adapting the business, the service transformation, sometimes digitally, sometimes with workflow transformation. But that opportunity and service is huge and almost never. There's no company I know of that's taking 100% of their service market share. That's the difference, especially in slower growth. Asset manufacturing are more mature verticals. >>So I was here last night walking the floor, and I went to the extent you Booth, you know, anytime you see, except you're in a show like this. Okay, Censure. You think Large company Global. I was actually quite impressed a little bit surprised to see you know, their presence here because they they go where the money is, right? And so my specific question is, think, except you think big companies. But you guys obviously focused on what range of companies smaller midsize company. So what's the landscape? Looked like? What's the difference is between sort of smaller and larger companies, >>so that's a great question. I'll take it in part So if you think about a neck censure definitely they looked a large. I also have had meetings with the Lloyd McKinsey Cap gem and I dxc etcetera Also tcs Tech Mahindra which a little bit or more telco focused. So if you think about at the very large and you have telco utilities, large manufacturing O e ems that our customers and definitely the customers I'm pursuing Maur with this focus But we also with work with go down to the S and B We had panels also of, for example, female owners of franchises and also males as well that are creating new service businesses and they're starting maybe with one truck in out providing service. So the fact that we can handle not only the breath and depth of complex service needs, but through work wave we also can encourage the small service businesses to reach their full potential is fantastic. And you know that makes me excited every day. And part of why I focused on service specifically is you are delighting customers. You are the face of a brand and you're making a difference. It's not something that s 02 is esoteric. This is about really value that we're delivering, >>always interested in the dynamics of serving the SNB market >>because one of >>these small companies don't really have that. Maybe family owned there found her own. They don't really put a lot of value on technology. How >>do you >>get in the door? How do you convince them that automating the service function is actually worth the investment? >>Well, first off, I'd say that even the big companies are struggling to go paperless. Okay, so, you know, I think some of the challenges we see survive, if you will, big to small, especially when you look globally in different countries. What have you. But the approach we take in the S and B is that we want to be a software as a service provider, and we were to really handle everything they need in their business. So everything from how they grow leads how they have c r m type functionality. How, then they're delivering service, how they're cross selling service, how they're billing service. So at the at the S M B level, we're putting that kind of all in one technology and there's really not that much integration or I T Service is around that right. We want it to be easy and fast, etcetera, as you go more into the mid market and then definitely into the enterprise. Then you start getting more complexity. You get more I t service's integrations, more configurable ity, sometimes even some customized software. So there is a definitely a difference in the complexity. But the fundamentals of what a service business needs really isn't that much different to your >>customers that you mentioned customize and you guys were SAS space. That's one of the text that we'd like to sort of explore a little bit. A lot >>of >>times SAS companies want to avoid, you know, custom mods. But at the same time, you guys are trying to offer a choice. So help us square that circle. How do you What's the conversation like with customers in terms of how you advise them, You guys obviously do a lot of deep functionality, you know? How do you sort of advise them whether or not to go heavily custom or try to go out of the box? >>Certainly. So in the true, I'd say the small business of a medium you start getting some crossover, but in the small business, Absolutely avoid customization because you won't be able to stay evergreen. It's going to be too hard to maintain. You don't have the subject matter experts, et cetera, so that's really a truce. Ask that from a community. A product engagement. We need to be driving the partnership with the customers that they can use a software out of the box in ways that matter to them. As you start getting into the mid market and especially the enterprise, then it becomes more of a choice, right? How much money do you have to spend? How robust is your organization and set trek? And in general, I advise customers if they care about evergreen software, et cetera. If they care about ease of upgrades, don't customize that Being said, we recognize sometimes in the field with your brand experience Custom mobile. You may need to customize a little bit, so it's Ah, say, a chicken and an egg. You have to weigh the benefits of the costs, and that's what we work through with our >>customers. Specifically morning. What's the upgrade cycle like? There's a customer having the choice Thio upgrade at a particular time, Or do they have a window? >>So it varies primarily, there's a few exceptions, but in general, with the work way, Family of products is true SAS. So it's almost like you're Apple Phone. We pushed the upgrade and you have to take it. Okay, And that's the true SAS model at I. F. S. And this is something Darren talked about in his keynote. We pride ourselves on offering choice. So even though we do have regular release cycles, we encourage customers to upgrade regularly. They have the choice on when they take upgrades and also how they deploy. We have some markets with things like data, privacy and what have you that they may, for that reason or for other reasons, go on premise even still today. So we give them the choice on how they upgrade as well as where they host. >>I'm fascinated by your product line. You have products for pest control. H V. A. C. Plumbing cleaning service is long and landscape. How different are these industries really in terms of their their automation needs? >>Well, I'll tell you one of the personal factors that Darren wanted to make sure I was comfortable with was multitasking. And that definitely is the case, because an I f s, we serve five key industries. So if you think about manufacturing utilities, telco service providers and Andy Okay, that's more at the enterprise level. If you think then when you go toe work wave. Those verticals that you mentioned are all the ones we service at work wave, and they are different. So you know what? Work wave. It's primarily service industries where you're going into ah, home and a little bit The commercial aspect and I effects were also doing more some heavy industries, some very large asset base, things like that. So I like to think about it as a product I service consumer based service. And then you can also differentiate across verticals with what are called high value assets versus, you know, Mork consumer size assets. >>So what >>are >>the one of the key technology enablers that are driving service management today? I mean, obviously, cloud, we talked about sas a lot of push on you X and customer experience, but what other key ones? >>So all the three that you mentioned mobile is huge. You know, Pete and even today, like I run. I work mainly from my phone, and that's really what people want. They want efficient work flows that are configurable on mobile, tied to the customer, the asset, the business. And that's an area that we're continuing to make investment. We also try to prioritize how we bring in the new technology trends into service. Because every technology trend that you see has applicable ity and service supply chain and how you run spare parts specially globally, you can see applications for Blockchain augmented emerged Reality how you can connect the field tech with an expert resource or remote resource to the consumer. That is obvious, right? So you talked about the enabling technologies like Cloud, how we're thinking about data platforms and Data's the currency. Of all of that, we need to d'oh. His service is really about a an execution engine, right? Because to deliver a customer experience that makes people come back to your brand. To purchase Maur, you need great service, so any time somebody talks about customer experience, but they don't talk about service. I want to say you're really naive because you can just get the customer. You have to delight the customer. >>Uh, the, uh, there's a lot of interesting technology going on now in the area. Fleet Management making fleets more efficient How does that figure into the service is? You offer. >>So Fleet management is an important part, and it's one that you have a very tangible return on investment when you deploy route management route optimization, fleet management. So you have the aspects that are very tangible, relate to how do you get the person or the truck where it needs to be when it needs to be okay, and that's pretty well understood. Then how do you get the most efficient schedule that minimizes miles driven gas, used et cetera? And then, of course, you also are thinking about health and safety. There's some cool things now that you can partner that if you have these fleet technologies installed in a way that is integrated in your service business, you can actually get lower insurance premiums, right? So it's not just the conventional use. Cases were starting to think in this kind of gig economy, how you can also be thinking about bringing in Maura what's called a contingent workforce. So if you have surge capacity in a certain period or you want to just do more third party service, probably your appliances. You know they're not the employees, if you will, of a g e or a world polar and LG right there Probably a contingent workforce. And that's a model that's also evolving. But to do Fleet Management across say, contractors, not just employees is an area that were thinking more and more led by some of the uber ization, if you will, of the of the marketplace >>right up against the clock, Marty. But to last questions You made an acquisition today, Vashti Uh, yeah, uh, I thought of it as a tuck in acquisitions, although Darren essentially sort of said, it's gonna make you the leader now in service management. Um And then I want to understand how you guys differentiate from some of the big whales. >>So, you know, overall, we're on track to be about 700 revenue this year in service management. We're working to get to 200 million, right? So this year will probably be around maybe 1/5 50 ish per se. Don't quote me on that check with our coms team, but the point being is that we have the ability to use these tuck in acquisitions and service to accelerate our lead, not just from a revenue perspective, which is what we were just talking about. But from a product perspective, you might have followed Salesforce acquiring Click. That means we are the only independent. Aye, aye. Optimization engine that is field tested. Battle ready. So that's great. This s t a is how we consolidate our dominance and complex service. So what darren was speaking to is not on Lee the service management segment of our revenue and how we continue to accelerate over the oracles in the S a. P s and the service maxes et cetera of the world. But how we take what we're already dominant in and really put the hammer down. Honesty is part of that. >>Your differentiation then if I infers, is focus. Um, you're you're deep customer customs agent deep >>domain expertise. Yeah, So really, when you think about a i optimization, which drives a ton of business value and the ability to handle the complex service cases that then drive business outcomes and outcomes based service models, we are number one and s dea tucks into that, even though it is very strategic on how we position ourselves with leadership and service. >>All right, Challenger becomes number one, Marty. Thanks very much. All right, Keep it right, everybody. Dave A lot with Paul Galen. You're watching the Cube from Boston Mass. I f s world 2019 right back.

>>live from Seattle, Washington. It's the booth covering smartsheet engaged 2019. Brought to you by smartsheet. >>Welcome back, everyone to the cubes. Live coverage of smartsheet engaged here in Seattle. I'm your host, Rebecca Night, along with my co host, Jeff. Rick. We're joined by Anna Griffin. She is the CMO of smartsheet. Thanks so much for coming on the Cube. Thank you, guys for having me appreciate it. So you were your pretty new to this company. Joined in April. I'd love to hear, but you've also had an illustrious career in marketing. You've worked summers and big names, including Apple and Nortel and and Saturn. And you've also worked for Land's end and a whole bunch of different varied career. What attracted you to smart? >>She You know, it was interesting when I first got the call about smartsheet. I had never heard of it. And the way that it was positioned to me was super intriguing. I realized it was one of those a category that's just not established, but a category that has the potential to be the next big thing. And we're not even the potential. I mean, it will be the next big thing and, you know, I met with that was intriguing. But then, you know, I met with the executive team, and it was a perfect combination of a killer product, but a killer company. I can't tell you how special the leadership of this company is and their authenticity and their passion and their drive and their belief. It's so contagious. There's no way you would not want to be a part of it. So on, then, the privilege to be able to tell this company's story I feel like it is the best kept story. Not only in Seattle, potentially the world on I plan to tell the story and And what a gift it was. A great opportunity is a marketer toe have this type of opportunity. >>Well, we're gonna get into how you're going to tell the story. Okay, See you later. But so now you've been here a few months. It is your first ever engaged. What? What does he what are your impressions? >>Well, I wish I had been thio previous engaged to have something to compare it to. But the fact that this conference has doubled in size 4000 customers here and it's only its third show. I will tell you in the industry I've worked, you know, managing events teams for many, many, many years. Not a lot of conferences grow at this size, and Soto have 4000 customers here who are zealots. They are their passion for the product and what it's doing and what it's doing for there. Not only their companies, but their own personal careers. There isn't an empowerment story through their mouse that will just inspire you. So it's It's incredible. The energy here is really, really especially. >>Feel it, too. Way See >>it a lot of the smaller conferences, early days. That's why they're fun to be. Here were last year, when those 2000 it was adjacent to the to the office across the across the water. Exactly, but it is a really passion community, and, you know, Thio here, literal, literal cheers and claps at features. Well, it's great to see, like copy paste from one road to the other because it's clearly something that means something these people and that they have asked for and the company is delivered and really demonstrates, is listening to engage these crazy people. It's a great asset >>wave. That listening thing is huge, and I feel like that's one of the things. And I think it's why there is a CMO now. That's why I get the privilege to be the first CMO is because the customers said way need more awareness of this company. We need our our executives. We need lines of business leaders. We need i t to know who you are and the magic of what you do. We need awareness is gonna make it that much easier for us to get much wider adoption across these companies. If people know who you are and they know you know what you're capable of. So listening. That is one of the number one things we've heard. It's like awareness. They wanted awareness. So because it'll help make them more successful. So I think that was the catalyst for Okay, let's get a cheap Marty Officer, Let's go build >>that about you. What are you gonna do? What it wanted? Some of your top priority is to tell the story and to build brand awareness. >>Yeah, well, um well, you're the first thing was to really kind of Titan are positioning again. It's a great great products make great brands, and this is a great product company. But man were starting to do so much ward than just killer products. We're really getting into this enablement this, um right, transforming companies. And so I wanted to make sure we're positioned properly. And we're really positioning mawr in a more transformative altitude and the capabilities of what we can do. We have found we've spent way too much time talking about technology versus people versus what technology and people are going to do together. And that is the magic of what smartsheet does. It really takes platform a common platform that basically integrates with the Czech investment that you've already made with the systems of record that you already have pools that data out and then allows the people I work with that data all in a common really time, you know, application. And when you can marry those two things together that tech and people, that's when one plus one equals three. And so we call that that three is what we really call achievement again, Like everybody in our space is work work, work, task management, project management, the capability of smart shit Yeah, we do all that, too. But when you play in that transformative altitude, we're in a bling achievement and it enterprise wide level and achieve it like what your business can achieve. But this is the more special part. And this is where I get excited. Did you feel to tell this story is the achievement happens at a personal level to like again? I'm telling you when I talk to customers and I see what they're doing right, you don't understand. You have changed my career. I'm doing more strategic work. I am. I am seen differently in my company. I champion this, like all of a sudden, I am leading big teams. I went from this to this, and there they're empowerment is so big and so really that last mile of digital transformation is cultural transformation. And that's what this product does. And so job one was position. That's properly so we can tell that type of story and really put our solutions in that kind of light because that's what it does on then job to is to launch the campaign launches to the world. So we just launched two weeks ago, and it's a slow roll. I mean, we have hundreds of assets, it in place. So if I love seeing us on television, you know I love seeing is deeply in digital. I love some of the new interesting things that that we could do in media. But when our customers are saying that, you know they're seeing it a CMO like you high from it. Yeah, So it's fun. So jump to launch the campaign and the campaign is, well, we call the campaign can do you know we're positioning the brand as the platform for enterprise achievement. Number one Smart sheet is a platform, I think a lot of people, you know as it's grown. I mean, it truly is a platform, and it really is enterprise strong and wide. It's skills which is important, but its scales So everybody and a company can align organizational alignment to truly achieve something bigger aligned organizations do not fill. And so that's the That's the power. But I digress. >>No question that way >>you know, one of the great legs of your of your go to market strategy and your lead jen is your licensing formula, which enables me as the Spartan sheet licensee to engage lots of people, many outside my own, not by my own team, but my own company. And let them have access to this tool. What a smart, smart waiver. Whoever came up with that licensing strategy? What a great way to introduce the opportunity to use this transformational tool to ongoing and broad audience. >>Yes, your table is so exciting. When I was in the interview process and I was riding on a plane and clearly I had met with the company and I heard somebody in front of me was a consultant, one of the consulting firms who had met a complete stranger on the plane. And somehow Smartsheet came up that she was going you got Oh, my gosh, Smartsheet. This is like she was going. This is the best kept secret. We're using it with all of our clients. We heard about it through one of our clients. That wasn't one of them. We'll use it like Oh, my gosh, this is the game changer. I'm like putting >>my here in between the wayward. I put my hand it as it did You just say smart shape. >>Literally six people on the airplane, random people like, Oh, my gosh, we use it to It was the most surreal experience, and that was when I knew, like, Okay, I've got to be a part of this Coast special. Did a lot of people are now just getting that sensation of what this thing is capable of. And, >>well, it's funny to your personal achievement story. Reminds me of any time you know you got a new software company and whether, you know, centered alloy Dorian, Why, when those guys come in, they're making a big bet right there. Some new partner's gonna bet. Bet their career on this new technology. We've heard from a number of people how betting their career internally with smartsheet has changed their position in the company. Yes, for that today, a couple of times. So clearly you know it. It is an enablement platform for someone to, you know, grab on to the to the rocket ship and ride this Marchi wave thio new and bigger, better things, >>but but also her point about just even just participating in the technology. And then they're able to, as you said, work on more strategic work, be able to do more things in their jobs that have been catapulted them to new job. So it's not even necessarily betting on smartsheet bringing in smart cheat. But it is just just using smart sheet and then therefore they have more brain time. Yes, yes, oh, engagement we're talking about, >>right, right? You know, it is because we've been talking a lot about you know, some of the really scary statistics about how disengaged people are at work and how many people are ready to quit their job. And, you know, they've got all these blocks. Is menial roadblocks in their day to day existence that are that are negatively impacting their ability to want to do their job or but actually just want to be there anymore. And so it's It's like seems maybe to the outside, looking in some of these things by seem low value, but they're actually tremendous value. If you're removing these roadblocks so I can get my job done >>totally and love your job, you love your job. But know that the work that you do matters and I think so many people have lost that feeling like there's something about working and I don't know if it's the corporate world. But it has become such a grind, and that rare opportunity would like. I love what I do, and I know that it matters. It's a gift, and this is a platform that enables bad in people. And so I think that's when the fascinating things I've been spending a lot of time on the road with customers and I was at a very big multi national, big global agricultural company. And, um, Singer, Actually, I'm watching WAY Bet with probably 200 different Just what I would call power users across seven different you know, roll types like from I t toe hsc thio, you name it. And, um, every single one of them is like art. We're doing more like we are in power, like the engagement, the employee engagement in that company through the roof because every single person felt like were hurt. I have ownership, you know. I'm doing work. I'm taking it to a new level. And so you know, sure, there is a Thanh of operational efficiencies that are gonna come out of working with smartsheet. But I think the one to watch is what's gonna happen when your workforce is truly engaged and taking ownership of the work. Those were the good. Those are the companies that are going to have a higher retention. They're gonna have you. They're going to see something in that in that talent area. So this is more than just We're getting more work done and return on investment of our our our systems, like you're going to see you know what happens when your when your employees are empowered. >>Well, the word you didn't use his innovation that I firmly believe everyone wants more innovation, their company. >>But how do you do >>it? One of those? I think it's really simple. Lever on that is you just get more people more access to more data and then the ability to do something about it and open it up to all the smart people that see problem to different prisons in different opportunities. And that's where you start to get in. A leverage is amazing talent that you already have inside your four walls. >>But what is interesting about innovation, as I think sometimes the world so over rotates that innovation is gonna be that next killer line of code or it's going to be and they forget that the power of practical innovation like it's that Siri's of small collected things at out up, allowing your entire, you know, employee population to feel like they have the power to innovate us. That every person in the company has the power because the power practical innovation can lead to something Justus Big is the big already. >>Dev. Ops has shown that that's a better way anyway, right in software development, with the grand idea with the market development plan and the product development plan and the three year build cycle that's does not win against constant religious narrative improvement. Improvement, improvement, improvement, improvement. Yes, >>indeed. So you and you said this earlier and I saw it on your Lincoln to the last mile of digital transformation is cultural transformation. How do you describe the culture at smart shape now? I mean, we've talked about the evangelical customers Yeah, about with in smartsheet itself. >>It's, um it's pretty special. Know what you're gonna say? Of course. And see him? I was gonna say special, but it is. It is rare when people everyone comes to work with this belief like this true belief that they are They have the power to influence something and touch something that's going to do something great for other people. And I think that's what eyes, the most specialised. They they're not just doing it for themselves. They know they're doing it for others, like they know they love these guys. Every single person in the company loves that customer like the love ability, They love the customer, and they feel like they've got to do their best work so their customer can do something great with it. You know, they really understand that, and that's Ah, it's an incredible place to wanna work when you, when you feel that way but toe love your customers. I think that's why our customers love us back and to be loved. You must first love and because they love you know, it's it's >>rare. Well, congratulations. It sounds like it's a great role and you're in the right place. And I can't talk to you next year and hear more about can do and and all of the wonderful things you're doing. Thank you. Thank you, guys. I'm Rebecca Knight. That wraps up the cubes. Interviews. Stay tuned for our rap of engaged 2019 you're watching the Cube

>>live from Seattle, Washington. It's the booth covering smartsheet engaged 2019. Brought to you by smartsheet. >>Welcome back, everyone to the cubes. Live coverage of smart. She engaged here in Seattle. I'm your host, Rebecca Night, along with my co host, Jeff. Rick. We're joined by Anna Griffin. She is the CMO of smartsheet. Thanks so much for coming on the Q. Thank you, guys for having me appreciate it. So you were your pretty new to this company joined in April. I'd love to hear, but you've also had an illustrious career in marketing. You've worked several big names, including Apple and Nortel and and Saturn. And you've also worked for Land's end and a whole bunch of different varied career. What attracted you to smart? She You know, it >>was interesting when I first got the call about smartsheet. I had never heard of it, and the way that it was positioned to me was super intriguing. I realized it was one of those a category that's just not established, but a category that has the potential to be the next big thing. And we're not even the potential. I mean, it will be the next big thing and you know, I met with that was intriguing. But, you know, I met with the executive team and it was a perfect combination of a killer product, but a killer company. I can't tell you how special the leadership of this company is and their authenticity and their passion and their drive and their belief. It's so contagious. There's no way you would not want to be a part of it. So on, then, the privilege to be able to tell this company's story I feel like it is the best kept story not only in Seattle, potentially the world on I plan to tell the story and and what a gift. But what a great opportunity is. A marketer toe have this type of opportunity. >>Well, we're gonna get into how you're going to tell the story, okay, a little bit later, but so now you've been here a few months. It is your first ever engaged What? What does he what are your impressions? >>Well, I wish I had been thio previous engaged to have something to compare it to. But the fact that this conference has doubled in size 4000 customers here and it's only its third show. I will tell you in the industry who have worked, you know, managing events teams for many, many, many years. Not a lot of conferences grow at this size, and Soto have 4000 customers here who are zealots. They are their passion for the product and what it's doing and what it's doing for there. Not only their companies, but their own personal careers. There isn't an empowerment story through their mouse that will just inspire you. So it's It's incredible. The energy here is really, really especially. >>Feel it, too. Way See >>it a lot of the smaller conferences early days. That's why they're fun to be. Here were last year, when those 2000 it was adjacent to the to the office across the across the water. Exactly, but it is a really passion community, and you know, Thio here, literal, literal cheers and claps at features. It's great. It's like copy paste from one road to the other because it's clearly something that means something these people and that they have asked for and the company is delivered and really demonstrates, is listening to engage these crazy people. It's a great asset >>wave. That listening thing is huge, and I feel like that's one of the things. And I think it's why there is a CMO now. Why get the privilege to be the first CMO is because the customers said way need more awareness of this company. We need our our executives. We need lines of business leaders. We need i t to know who you are and the magic of what you do. We need awareness is gonna make it that much easier for us to get much wider adoption across these companies. If people know who you are and they know you know what you're capable of. So listening. That is one of the number one things we've heard. It's like awareness. They wanted awareness, so because >>it'll help make them >>more successful. So I think that was the >>catalyst for OK, let's get achieve, Marty. Officer, Let's go build that about you. What are you gonna do? What were some of your top priority is to tell the story and to build brand awareness. Yeah, well, um well, you're the first thing >>was to really kind of Titan are positioning again. It's a great great products make great brands, and this is a great product company. But man were starting to do so much more than just killer products were really getting into this enablement this, right, transforming companies. And so I wanted to make sure we're positioned properly. And we're really positioning mawr in a more transformative altitude and the capabilities of what we could do. You know, we have found we've spent way too much time talking about technology versus people versus what technology and people are going to do together. And that is the magic of what Smartsheet does. It really takes a platform, a common platform that basically integrates with the tech investments. And you've >>already made with the systems of record that you already have pools that data out and then allows >>the people I work with that data all in a common really time application. And when >>you can marry >>those two things together, that tech and people, that's when one plus one equals three. And so we call that that three is what we really call achievement again, like >>everybody in our space >>is work work, work, task management, project management, the capability of smart shit. Yeah, we do all that too. But when you're playing that transformative altitude, we're in Ebeling achievement and it enterprise wide level and achievement, like what your business can achieve. But this is the more special part, and this is where I get excited. Did you feel to tell this story is the achievement happens at a personal level to like again? I'm telling you when I talk to customers and I see what they're doing right, you don't understand. You have changed my career. I'm doing more strategic work. I am. I am seeing differently in my company. I champion this, like all of a sudden I am leading big teams. I went from this to this, and there they're empowerment is so big and so really that last mile of digital transformation is cultural transformation. And that's what this product does. And so job one was position. That's properly so we can tell that type of story and really put our solutions in that kind of light because that's what it does on then job to is to launch the campaign launches to the world. So we just launched two weeks ago and it's a slow roll. I mean, we have hundreds of assets it in place. So if I love seeing us on television, you know I love seeing is deeply in digital. I love some of the new interesting things that that we can do in media. But when our customers are saying that you know they're seeing it, a CMO like you gonna get a high from it. Yeah, So it's fun job to launch the campaign, >>and the campaign is, well, we call the campaign can do you know we're positioning >>the brand as the platform for enterprise achievement. Number one Smart sheet is a platform, I think a lot of people, you know as it's grown. I mean, it truly is a platform, and it really is enterprise strong and wide. It's skills which is important, but its scales So everybody and a company can align organizational alignment to truly achieve something bigger aligned organizations do not fill. And so that's the That's the power. But I digress. >>No question that way >>you know, one of the great legs of your of your go to market strategy and your lead Jen is your licensing formula, which enables me as the Spartan sheet licensee to engage lots of people many outside my own, not by my own team, but my own company. And let them have access to this tool. What a smart, smart waiver. Whoever came up with that licensing strategy? What a great way to introduce the opportunity to use this transformational tool to ongoing and broad audience. Yes, >>your table is so exciting. >>When I was in the interview process and I was riding on a plane and clearly I had met with the company and I heard somebody in front of me was a consultant, one of the consulting firms who had met a complete stranger on the plane. And somehow Smartsheet came up that she was going you got Oh, my gosh, Smartsheet. >>This is like she was going. This is the best kept secret. We're using it with all of our clients. We heard about it through one of our clients That wasn't one of them. We'll use it like Oh, my gosh, this is the game changer. I'm like putting my here in between the wayward I put my hand in as it did You just say smart shape. Literally six people on the airplane, random people like, Oh, my gosh, we use it to. It was the >>most surreal experience, and that was when I knew, like, Okay, I've got to be a part of this Coast special. Did a lot of people are now just getting that sensation of what this thing is capable of. >>And, well, it's funny to your personal achievement story. Reminds me of any time you know you got a new software company and whether you know, center Deloitte or even why, when those guys come in, they're making a big bet right there. Some new partner's gonna bet. Bet their career on this new technology. We've heard from a number of people how betting their career internally with smartsheet has changed their position in the company. Yes, we find that today a couple of times so clearly you know it. It is an enablement platform for someone to, you know, grab on to the to the rocket ship and ride this Marchi wave thio new and bigger, better things, >>but but also her point about just even just participating in the technology. And then they're able to, as you said, work on more strategic work, be able to do more things in their jobs that have been catapulted them to new job. So it's not even necessarily betting on smartsheet and bringing in smart cheat. But it is just just using smart sheet and then therefore they have more brain time. Yes, yes, oh, engagement we're talking about, >>right, right? You know, it is because we've been talking a lot about you know, some of the really scary statistics about how disengaged people are at work and how many people are ready to quit their job. And, you know, they've got all these blocks. Is menial roadblocks in their day to day existence that are that are negatively impacting their ability to want to do their job or but actually just want to be there anymore. And so it's It's like it seems, maybe to the outside, looking in some of these things by seem low value, but they're actually tremendous value if you're removing these roadblocks so I could get my job done >>totally and love your job, you love >>your job. But know that the work that you do matters and I think so many people have lost that feeling like there's something about working and I don't know if it's the corporate world, but it has become such a grind and that rare opportunity. We feel like I love what I do, and I know that it matters like it's a gift and this is a platform that enables bad in people. And so I think that's when the fascinating things I've been spending a lot of time on the road with customers and I was at a very big multi national, big global agricultural company. And, um, Singer, Actually, I'm watching WAY Bet with probably 200 different Just what I would call power users across seven different you know, roll types like from I t toe hsc thio, you name it. And, um, every single one of them is like art. We're doing more like we are empowered, like the engagement, the employee engagement in that company, through the roof because every single person felt like were hurt. I have ownership, you know. I'm doing work. I'm taking it to a new level. And so you know, sure, there is a Thanh of operational efficiencies that are gonna come out of working with smart shape, But I think the one to watch is what's gonna happen when your workforce is truly engaged and taking ownership of the work. >>Those were the good. Those are the companies that are >>going to have a higher retention they're gonna have >>They're going to see >>something in that in that talent area. So this is more than just We're getting more work done and return on investment of our our our systems like you're going to see you know, what happens when your when your employees are empowered. >>Well, the word you didn't use his innovation that I firmly believe everyone wants more innovation, their company. >>But how do you do >>it? One of those? I think it's really simple. Lever on that is you just get more people more access to more data and then the ability to do something about it and open it up to all the smart people that see problem to different prisons in different opportunities. And that's where you start to get in. A leverage is amazing talent that you already have inside your four walls. >>But what is interesting about >>innovation is I think sometimes the world so over rotates that innovation is gonna be that next killer line of code, or it's going to be and they forget that the power of practical innovation like it's that Siri's of small collected things at out up, allowing your entire, you know, employee population to feel like they have the power to innovate us. That every person in the company has the power because the power practical innovation can lead to something Justus biggest the big already >>Dev Ops has shown that that's a better way anyway, right in software development, with the grand idea with the market development plan and the product development plan in the three year build cycle that's does not win against constant religious narrative improvement. Improvement, improvement, improvement, improvement. Yes, >>indeed. So you and you said this earlier and I saw it on your Lincoln to the last mile of digital transformation is cultural transformation. Yes. How do you describe the culture at smart shape now that we've done talked about the evangelical customers Yeah, about with in smartsheet itself, it's, um it's pretty >>special. Know what you're gonna say? Of course. And see if I was >>gonna say special. But it is. It is rare >>when people everyone comes to work with this belief like this true belief that they are. They have the power to influence something and touch something that's going to do something great for other people. And I think that's what is the most special is they? They're not just doing it for themselves. They know they're doing it for others, like they know they love these guys. Every single person in the company loves that customer like the love ability, They love the customer and they feel like they've got to do their best work. So their customer, I can do something great >>with it. You know, they really understand that. >>And that's Ah, it's an incredible place to wanna work when you, when you feel that way but toe love your customers. I think that's why our customers love us back and to be loved. You must first love >>and because they love you know, it's it's rare. Well, congratulations. It sounds like it's a great role and you're in the right place. And I can't talk to you next year and hear more about can do and and all of the wonderful things you're doing. Thank you. Thank you, guys. I'm Rebecca Knight. That wraps up the cubes. Interviews. Stay tuned for our rap of engaged 2019 you're watching the Cube

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum, World 2019 brought to you by VM Wear and its ecosystem partners. >> Welcome back. We're here! Mosconi North for VM World 2019 10th Year of the Cube covering VM World. I'm stupid and my co host is John Troyer. And welcome to the program to guest from Del Technologies. Sitting to my right is Tender, my Mondal, who's the director of storage solutions and sitting to his right is David when the senior director of server, product planning and management also with Dell. Gentlemen, thanks so much for joining us. All right, so we've got server and storage and talk about something that we've been talking about for a while on the server side been delivered for a bit and on the storage side is now rolling out. So everybody's favorite topic. Nonviolent till memory express or envy me as it rolls off the tongue storage class memory, or SCM and lots of other things, you know, down there, really helping a big, transformational wave that, you know, we really changes how our applications interact with the infrastructure channel, you know, bring us up to date on the latest. >> Sure on, let's start where you ended. We're seeing explosion off applications, right? And in fact, in mornings, keynote. Bad girl singer had a stocky speaks. There are 352 million enterprise applications today. On it will be 792 million in three years. Now, as the applications are growing exponentially, we cannot keep growing the infrastructure at that rate, So N v m e is the way we can consolidate it. Ah, lot off the infrastructure. If we can think about in tow and envy, Emmy starting from the server in fear me off our fabric through the stories area down, toe the back end with envy Emmy necessities. This actually can put together a great platform where you can consulate it. Ah, lot off the applications and delivering the high performance low latency that will need while meeting video surfaced level objectives so we can go over a little bit off the details, but I think it all starts from envy me over fabric coming from the server to the story, Ari. So probably like that's the fourth step we need to consider >> David. Do You know, I love this discussion when we get to talk at the application later because, you know, Flash changed the market a lot. You know, it's like, you know, much better energy, and it's much faster, Anything. But you know, this inflection point that we're talking about for application modernization, you know, envy me is one of those enablers there and something they know your team's been working on >> for a while. Yeah, actually, on the power each side we've been, You know, we've been embracing the benefits of enemy for quite some so many years now, right? We start out by introducing enemy in our 12 generations servers, you know, frontloaded hot, serviceable drives. And then, of course, we branch out from there on in today, you know, Ah, a lot of the servers from a Polish family all support enemy devices. So the benefit there is really giving customer choices in terms of what kind of storage kind of cheering they wanted, you know, for the applications needs. Right now, one of things that's great about, you know, enemy over fabric is it's more than just a flash storage itself. It's about enabling the standards, you know, across the host across the data fire Break down to the storage really to deliver on the overall performance that you know the applications of needs and buy, you know, improving I ops and lower late, Easy overall, from a server perspective, this just means that we're releasing more CPU cycles back into the application so that they can run different types of workloads. And for us, this is this is a great story from power. Just was from Power Macs and coming together to enable this Emmy, Emmy or fabric. >> You know, I'm I'm I'm kind of slow about some of these things, but if you kind of squint at the history and, you know, we went from the PC revolution and then we had, you know, we had Sands and raise right and we had we had centralized toward shared storage last couple of years, a lot of interest and stale right hyper converged. And you had a You had a lot of pizza boxes with the storage right there. It's I mean, I now think right and I'm following the threat, I think which is now that where we now can have ah, Iraq with again a fabric and and again, now we can We can focus on our envy me storage over our envy me over fabric driven, solid state storage somewhere below my servers that are that are doing handling compute somewhere else. Is that that the future we're headed towards now >> Yes. I mean, everything has its place. But to give you the perspective, right? It's not just, I mean coming down to the storage area, but how This is enough bling, the future storage as well. And the storage class memory is the perfect example. And as Defeat said, let's take power, Max, as an example, right. Eso in power Max, you can It is like entrant, envy me ready like you get envy emi over Fabrica de front end But then we have n v m E s s trees in the back end. The thing is now it is also the N v m e is enabling technologies like stories class memory which is bringing in very high performance, very less latency Latency is going down in the order off like tents off microseconds. Now this is as close as you can get. Tow the like Dedham with persistent story. However, you need a balance. This is like order of magnitude are costlier. Now you got bar Max. What we're doing in terms of first, it's envy me. Done right? What do you mean by that? You have, like, Marty controller architectures that can actually do this level of parallel processing and our concurrency. And then we have bought, like, ECM for storage, class, memory and envy, Emmy essences. And we're doing intelligent tearing best on the built in mission learning engine that we have. And it is looking at 40 million data sets. Really time to decide. Like which sort of walk lords should go on this same drives which should go on and the M. E s estates. And on top of it, you add quality of service. So this platform gives you are service level objectives. You can choose from diamond, platinum, gold, silver or bronze, and you can consulate it. Ah, lot off those 352 million different types of applications on this area guaranteeing you are going to meet all off your SL s, no matter what type of applications they were consolidated into. >> Okay, I'm wonder if you could boast. You know bring us into what this means for VM wear customers and break it into two pieces. One is kind of a traditional virtualized shop. And secondly, you know, spend a lot of time in the keynote this morning talking about the cloud native containerized, you know, type of environment. Will there be any difference from from both of your world? >> Yeah, absolutely. I'm glad you brought that up because, you know, from from our perspective, right, what we've seen with the enablement of enemy platforms. You know, John, you brought up a very interesting point, right? It seems like you know, past couple years, we went from moving storage onto the host and now would envy me with fabric. We're actually taking the storage away from the host again. Right? And that's exactly true, because, you know, the first, the first statement you brought up stew. It's about how flash enabled different applications to run better on the host. What? We see that still right? And so what enemy? You know, we see the lower response time enabling our customers Thio run more jobs and more v ems per server. That's one aspect of it. You know, we've seen his benefit a lot of our platform today or using various different applications and solutions, and you talk about the ex rail that's a visa and story for Del. You Talk about Visa and ready notes for customers who want to build it themselves. Right platforms enabled would envy me back playing enemies. Storage allows them to use enemy or SAS sata whatever they want. But the point is, here is that when they're using every me flash, for instance, and I'll talk a little bit about the power climaxed with this all flash, uh, me back plane in a case in the study that we did with V San application running, oh ltp type of workload, we saw the response time with every me over traditional SAS, you know, from our competitors improved by 56% right, which means that from that same particular solution build out, we were able to add 44% more of'em on the platform. Now, at the same time, we increase the overall orders per minute by roughly over 600,000. Oh, pm's for that type of, uh, benchmark over our nearest competitors so that right there is the benefit that we see from my virtual eyes from, Ah, being where perspective >> on. I'll add from the storage perspective in two ways. In fact, in last vehement in a MIA, we demonstrated in tow and envy, EMI over five break up with special build off this fear supporting Envy me over fabric and stories. Class memory with envy Me drives what it gives you a regular like this fear best environment is that you have the ability to move your PM's around like the applications where the highest performance and Latin's is critical. It will be on those special service levels and special like de testers. In fact, that demonstration was like ECM did a store, and in P m E Sense media does so in the same fabric with in Bar Mexican moved things around, whether it's like regular Fibre Channel or CNN and then the other part. I want to add in the morning like we saw the announcement that now communities is built in or will be built in with the years Excite platform, right and you're sexy is bread and butter off all the storage customers that we have now with like when you consider those, uh, those things built in under this fear black from Think about, like how many applications? How many actualized workloads you can run, where that it's on premise or humor. Cloud on AWS. All of those consolidation, as well as like the performance needs while reducing your footprint does the benefit of the V M R R shops. But the PM admits are going to see from the storage site >> again. I'm not following the parts, but what kind of we're not talking about a couple of megabytes here anymore, Right? What size of parts are shipping these days? So >> So, from our perspective, up to 77 gigabyte actually start. Seven terabytes drives are available on the markets today for Envy Me Now, whether customer by those drives, you know, it depends on economic factor. But yeah, it's something that's in this available from Dell >> so on. I'll act to what David said so far in CM drives 750 gig to 1.5. Articulate a C M drives on Dwell ported often drives that will be available in the power Max Acela's 15 terabyte envy EMI assistants. So this is the capacity we're talking about. And again the Latin's is at the application level, like from the storage like you're going to see, like, less than 300 microsecond. That's the power we are bringing in with this technology to the market. >> Give >> us a >> little look forward we talked about, you know, envy me has been shipping for a bit on the servers now, really rolling out on the storage side, I saw there's a lot of started from the space. You know, one recent acquisition got guts and people talking. What? What should we be looking for from both of you over kind of the next 6 to 12 months. >> So over next to a next 6 to 12 months, he will see a lot of innovation in this case from the storage site where wth e order of magnitude. I mean, the one single Ari, I mean, today it supports, say, like, 10 million I offs less than 500 microsecond latency. Ah, I cannot give you the exact details, but within like, a short time, these numbers are going to go up by more than, like, 50%. Latency is goingto get reduced. The troop would will be driving will actually like more than double s o. You see, like a lot of these innovations and kind of like evolution in terms off the drive capacities both from the CME, drives perspective. Envy me, assess these. Those will continue to expand, leading to foster performance. Better consolidation, Uh, for all the workloads. >> Yeah, from our perspective, I mean, you know, data growth is gonna continue. We all know that, And for us, it's like designing systems based on what the customers need, what the applications needs, right. And that's why we have different types of storage available today. So for us, you know, while we're doing a lot of things from a direct attached storage perspective, customers continue to have a need for share storage. EMI over fabric just provides a better know intense story for us, really from a Power edge and Power Macs perspective. But in the future, you asked what we're going to do. Well, we see the need to probably decouple stories, class memory from the host again. And really, what's preventing us from doing today? It's really having the right fabric in place to be able to deliver to that performance level that applications needs. MM evil fabrics, fibre Channel Ethernet ice, scuzzy or I'm sorry, Infinite Band, whatever. These are some of the things that you know we're looking forward to in the future to make that that lead. All >> right, well, it's really been great to see technology that I know the people that build your products have been excited about for many years. But rolling out into the real world deployment for customers that will transform what they're doing. So for John Troyer, I'm still Minuteman back with lots more coverage here from Be enrolled 2019. Thanks for watching the Cube.