Hey guys, what's up? My name is Isaac and welcome back to the channel. Today we're gonna be talking about a question that I actually received via Instagram and I cut it down for you guys a little bit because it's a longer question but I think it's helpful for us to talk about this question on it because I think maybe some of us might be feeling the same thing. So let me read it and then we'll get into it. So the question goes like this, why does God feel so far away? I've tried to read my Bible and pray but I'm getting tired of the feeling like no one's listening. I know it's not about the feeling but I guess I wonder why God wouldn't just give me the feeling all the time to confirm that he's with us. He is all powerful, he can do what he wants, can't he? I'm just tired of feeling like God is a million miles away. Now this isn't the first time I've received a question with this kind of same sentiment, this idea that why does God feel far away? Like I read my Bible, I pray, I feel like well I am and like I know I'm a Christian or I'm pretty sure you know and but yet God feels far away. Why is this, why isn't this feeling like of closeness with God? Why don't we have that all the time? So if you've been living the Christian life for any amount of time you realize that that feeling of closeness, intimacy, relationship, the feeling of that with God it doesn't always stay as powerful as we may like it and that's the problem is that we desire that we would like to feel close to God all the time. We would like to feel his presence with us, we'd like to feel that connection and intimacy with him all the time but the problem is we don't. We love this kind of spiritual high that we get. Some people go to conferences to get it, some people it's a Sunday morning worship does it for them but midweek it's a challenge to feel like God is present with us. We try to read our Bibles and pray more in hopes that reading and praying will give us a taste of the transcendent God but it ends up feeling more like homework. We want to feel again, we want to feel that connection, that closeness, that excitement but often it's taken away and we don't experience it all the time but the problem is is that when we're in this space oftentimes we desire the feeling more than we actually desire God. We desire the feelings associated with a close relationship with God, that feeling of acceptance, that feeling of love, that feeling of mercy, of connection, of relationship, of intimacy with God. We like that feeling but oftentimes we forget about God. Yes the feelings are great but we oftentimes are seeking the feeling more than we're actually seeking God in relationship with him. You know when we dig deep into our those desires that we have our desire isn't actually for God, it's for us. We desire that we want to feel better, we want to feel good but you know what? That's not what we should be searching and seeking for. You know what? What actually God desires for us is that we would pursue him despite how we feel. Not searching after a feeling to make ourselves feel good but to further pursue him out of a love and trust in him because you know what? That's the foundation of our faith that's God. It's not our feelings because you know you're going to experience and if you've been a Christian for any length of time you've probably experienced excitement and joy and peace and that closeness and connection and relationship with God and that's a wonderful gift that God gives us but you know what? You're going to experience that that's not always there. You're going to feel a little bit far away from God but what you need to have trust and faith in is that if you were continuing to pursue God regardless of how you feel you were in that relationship with God and God will never leave you nor forsake you. You know if God feels far away from you right now if you feel like he's not there he's not present with you number one repent repentance. Say God hey I know I've made mistakes in my life I know I've sinned against you a good and holy God please bring me in right relationship with you because our sin separates us from God put your trust in Jesus Christ know that once you put your trust in him and you begin to follow him he will never leave you nor forsake you he will not leave you behind he will not even though he may feel far away at times he is not distant he is present with you. Now my encouragement to you this week is this when feelings are there praise him and rejoice but when feelings fade trust in him because he will never leave you nor forsake you. Now don't take my word word for it trust him. Thanks for watching guys if you enjoyed this video or found some insight from it please give the video a like and subscribe to this channel if you're looking for more videos that are centered around becoming disciples of Christ and following him as disciples daily. So if you're interested in that please subscribe and check out dailydisciple.ca for new articles and videos every week. Yeah guys I look forward to another video with you but I wish you the best as you go along this week. Thanks guys and I'll see you later. Bye.


Good afternoon, Supercomputing Nerds, and welcome back to Denver, Colorado. My name is Savannah Peterson, and we are here live for four days from Supercomputing 2023, joined by my fabulous co-host, John Furrier. John, how was your lunch? How's your afternoon? Lunch was great, and again, the pace of the play here at this conference is very fast. It's got the AI theme, but it's also blending younger generations, and it's basically a systems, I would call it a systems architecture, hardware meets software, reconfiguration, and it's still the same game. Hardware, middleware apps, storage, networking servers, and all kind of coming together kind of refactored, and it's going to be a great guest here as we get in to some of the elements that are being used to kind of build a new AI environment. AI hardware is all the rage, and Dell's been a leader in the moving fast, establishing a strong position. We love our friends at Dell, and without further ado, Bill Leslie. Welcome to the show. Thanks so much for being here with us.  This show is kind of the ultimate celebration of humans and computers interacting. That's right. What's it like for you to be here with our community? No, this is a blast, first time here with you guys live, so really enjoy myself, and really just being around the floor, seeing all of the universities that are out here, seeing more GPUs than I've seen in my life, it's fantastic. Likewise, I saw, instead of Got Milk, I saw Got GPUs as one of the t-shirts, and I thought, wow, we are really having a GPU party. I love the clever HCI human computer interface, because the hyper-converged infrastructure we had in the last session really kind of ties in the personalization aspect, because one of the things coming out of this show is that it's not just hardware show, or super computer chip show, it's actually showing outcomes around the new model of LLMs. A hyper-converged storage and server's coming together. That was, you know, a generation ago, maybe a generation and a half. Now it's like, okay, it's in the play, but it's actually has to deliver precision, but yet deal with broad sets of data, and this is where I think the AI infrastructure piece leverages kind of the best practices of HCI, but the game is changing. So give us your take on how, you know, the hyper-converged market's changing. Yeah, so one of the things that we saw with VxRail when it was introduced about seven years ago is it was really about how do we offload some of the day-to-day tasks, the mundane things that take up our time, right? And how can we automate, orchestrate, do those things that are the regular, reoccurring things so that IT teams can go out and actually create new ways of delivering their services? Little did we know we'd be where we're at today, where now AI is really radically changing that game in many ways with some of the large language models and things of that sort as well. I just want to, just for a second there, but I'm curious, you've been in the industry for a while. Were you anticipating this type of jump with AI right now? Were you able to see this in your crystal ball or has it all kind of come upon us? I think about four years ago, we started to see more and more of the GPU introductions that would take on new workloads, right? I think we were seeing obviously VDI use case with GPUs forever. And when we started to see, okay, we're going to start doing some things that might be some machine learning, some dabblings here and there on some workloads, we started thinking, okay, what else can we be doing to offload the next level with some of that? And one of the things we've been doing with VxRail along the way is building in capabilities where we're actually deploying those GPUs in the first run experience, managing the day two experiences with the life cycle management. Those things that sound easy, but can be kind of hard if you're not focused on that all the time. Talk about the workload changes around the end to end because one of the things, again, that's coming out of this and this came up at KubeCon and the Linux Foundation around Kubernetes is as these clusters start to get deployed, you got to start thinking about storage and the interconnects around it. And then you got to look at overall throughput and workloads, when are tokens being delivered if you're talking about LLMs, get retrieval models. So you now have this view of, it's not just the shiny new toy, the GPU or the model or the foundation model, it's an end to end workflow. VxRail and vSAN, all these kind of, I won't say old technologies, but the ones that were pre AI, quote, infrastructure have in place some of those things. They had workload, they thought about workloads end to end. AI though is going to the same direction. So what's the new workload dynamic that AI is addressing and how are you guys flexing your VxRail and the technology? Yeah, one of the things, John, that we're seeing VMware push the envelope in is their new architecture with vSAN ESA, right? They moved to a single tier, they removed that cash drive. This did a few things to help improve not only performance, but also improve the cost profile of the environment. Two key fans of the entire show. You don't often get the two for one type of the situation. And what we were starting to see even in the previous generation of vSAN OSA, their original storage architecture, was it was starting to top out some of these networks, 25 gig networks. Well, we wanted to really see what vSAN ESA could do when we push the envelope with 100 gig networking, which is small fry compared to some of the things that we're seeing here this week. But it's that next jump in evolution of performance and speeds that are really needed for those next gen workloads. How do we have those pipelines for all of that data that's going to be massively flowing across the network with HCI and similar types of technologies? It never was huge. It never was huge. I asked a couple of guys yesterday and Gals on theCUBE yesterday. If you could optimize for one of two things, more compute, more GPUs or more networking. Most people right now are into the GPUs, but they want more networking. They want faster network, they want interconnects because it's what's going on around the GPUs now and the CPUs is the big conversation here. It is, and actually one of the things that we've seen with our partner in Intel is their new AMX technology, the on chip accelerators. It's incredible for what it's doing for some of these AI workloads. Compared to the previous generation, you're getting like three acts, the type of performance for the BERT type models that are out there or image classification models with ResNet 50. This is just a game changer with what you're getting included in the fourth gen scalable prox. It's making it a reality where all of this confluence of tech is coming together for these next gen workloads like AI. I mean, high performance networking should be a show in and of itself because there's a lot of networking going on. Maybe that'll be next. I mean, I was talking with some of the storage folks and the conversation is it's not about storage anymore because storage is everywhere. You got to store stuff. It's about computing, accelerated networking. So it's a workload conversation, not so much a point solution because we're already penetrating with storage. Well, I think we sometimes overlook where the compute is going to as well. We've got a couple of customers that have hundreds, thousands of sites. So when you start dealing with 1,500, 10,000 nodes across all of these distributed locations with the compute, guess what? You got to do AI there. You got to be doing the visual queuing, the inferencing. What are customers doing in retail locations? It's not always about the biggest super computer you can get. It's about how you can get that compute right there at the edge. And optimize that action that you need, that workload or that batch or whatever that is. Let's talk about it. You brought up customers there. Are you sensing, I feel like you can almost feel it in the room. There's a lot of FOMO going on. Is that a conversation that you're having with your customers? How are you helping them navigate this entry? If they're not. So one of the things that we're starting to do, at least in the VX rail space, is see what you can get with what's out of the box. You might be amazed by what you can get with just the AMX chips that are part of the 4th Gen Prox. And then you can decide how much you need to go with the GPUs, right? If that 3X improvement is good enough for what their environment is, stick with it. If you need more than that, go for it, right? A lot of our platforms support GPUs. You can be GPU ready if you need to. We see about 25% of our customers right now starting to use GPUs here and there and some of those nodes. So it's going to be a big change for us going forward. That's a significant percentage.  Right? So you have modern databases are out there. We got a multi-cloud, we call super cloud emerging. You're starting to see, kind of like NVIDIA enabled like Core Weaver. That's an interesting power dynamic. You're going to start to see entrepreneurial activity come out. You guys see this end to end workflow. So I got to ask you, when you think about the cloud relationship, we're seeing a lot of on-premise action. We had a little debate with Vulture, but he thinks there's more repatriation going on than our numbers say, but he's in the data center business. So, you know, that he's a cloud service provider. So I'm sure he says that, but I think the repatriation numbers don't show the true trend, which is it's not about taking work was off the cloud. It's the net new build out going on. And this is the hybrid on-premise edge conversation we've been having for over a year. Now with AI, that's highlighted. Can you share your reaction to that? Do you agree? And if so, what other commentary can you add on top of it? Yeah, I think one of the things we've seen with VxRail, and we've been doing this with VMware Cloud Foundation on VxRail, is getting that most consistent operating model. What do you have in the cloud on-prem? How do you drive consistency? And Dell has introduced a couple new offerings in our Apex Cloud Platform so that we can now drive consistent cloud with Microsoft Azure, with our ACP for Microsoft Azure offering. We've also been doing similar with Red Hat OpenShift, right? And we're going to continue to push into those new forays because customers need the same experience. What they're getting public cloud, bring it back on ground. How do you make sure that you're delivering the same type of operating environment in both locations? Savannah, you know, we've been talking a lot about Dell here. Obviously they're sponsoring the booth and we want to shout out to Dell Technologies for enabling us to do our show here. But you mentioned VMware, and usually I'll be owned by Dell, but now they're going to be bought by Broadcom. So that's going to close. But if you look at VMware, we had reporters in Barcelona for their show. It was packed. And it's not because they have vSphere, that's their install base. There's a ton of excitement around the AI enablement coming out. So how do you see VxRail, VMware, and with a faster ethernet, obviously Broadcom's involved in that too, by the way. So you kind of have a new configuration of players in that game to give the lift to the old school vSphere. And so AI helps legacy, right? And so the excitement of VMware, I'm not sure it's because of vSphere, okay? vSphere's an install base. It's like an operating standard, but they're excited about what's going to happen beyond vSphere. I think what you're seeing in some of that excitement is it's on how easy VMware makes it to work with that existing stack of components and software, right? And then it's adding in the new workloads, whatever the AI inferencing models or visual models or large language learning type things. When you make it easy, like what they're doing with the VMware private AI, right? That's just bringing, setting it right there on the stack that you're already familiar with, the vXRail's right apart of what the cloud foundation environments, it's just start building and going. Bring that new workload set into the environment. I grew with the CEO, it was very technical. He took over the helm. Man, what a pivot they made, Savannah. They really added that on there. And they got people excited. We were involved in that with our super cloud project. But I think this points to architecture, okay? And if you look at the enterprise now, we've had this same exact conversation in KubeCon. Platform engineering is the hottest thing in that cloud native world. Basically that means that's the modern IT architecture. What is that going to look like? Because you guys have a position with vXRail and a lot of instances, a lot of customers, you got the vSAN, you got the VMware review, you got the networking. They're 20,000 customers. What's the architectural conversation going on in the enterprise right now? To make sure they don't miss the AI wave. Yeah. Well, and thank you for mentioning our 20,000 customers. They've been fantastic. An impressive number. And inside the VMware ecosystem of HCI, Dell is about two thirds of that install base. So we are right there in lockstep with what VMware is doing, has been doing with Tanzu, previously pivotal before that. And the Tanzu ready architecture is actually still based off of vXRail. So the platform underneath that infrastructure layer is set so that customers can just start using it. And it's really quite wonderful to see how many customers are adopting Tanzu as well as other Kubernetes frameworks with vXRail, whether it's EKS anywhere with Amazon or some of the Susie ones. It's really quite phenomenal what all we're doing there. And our 20,000 customers is extremely impressive. I see here in my notes to 277,000 nodes. We are not talking about small amounts here. That is very much at scale. And I think it's super impressive. And to be able to get those people and those customers onboarded to achieve their goals that quickly, that ease of use conversation has been such a hot theme here. Everyone wants to get from zero to 60 as quickly as possible. You mentioned just before the cameras went live that you have a nine year old son, Andrew. Yes. And since we're talking about making things easier, I am very curious, how do you explain super computing and what's going on with AI to Andrew?  He's actually written out resumes before using the words that we use, not knowing how they go together, but he actually strings it together pretty well because when you break it down to the simplicity of how these things work together, right? You've got to be able to use your brain. That's the compute in the system. You've got to know how to assemble the equation because he's starting to put together the math equations. This is kind of what HCI is doing at that infrastructure layer with the software. And then it's a, okay, now, how do we make you smarter? That's where some of the AI stuff comes into this, right? So when we make it very tangible for a young one, and we've got student groups that are around on the show floor today, it's really fun to see their eyes light up when those dots get connected on just how simple it can be. Yes, they're very complex things, but when we can make it simple for them, it's really just quite wonderful to see. You know, I like Savannah bringing up the human-computer interaction angle because, you know, that example is not only the younger generation, it's how they're going to work. So the interface of AI that we've all seen with Cheshire PT checks the box there. Your son's never going to learn data structures, okay? He doesn't have to. This stuff's going to be co-pilot for him and he can stand up Kubernetes clusters with voice activation, potentially. Load that VX rail. That's what it's going to be doing by 10. Programmable. At this rate. I was blown away when I saw 3D printing. Can you imagine an IT environment where you just say, deploy VX rail across this environment and manage the data pipelines from XYZ data sets? That's coming. It'll be here before we know it with it. And one of the things that we've been doing with VX rail is expanding all the types of flexibility that we have with deployments, right? We have compute-only nodes that we call dynamic nodes. So if you just need to add additional compute to an environment, you can go and load your dynamic nodes. They have a personality of sorts for what that first run experience needs to look like. We have satellite nodes for just a single node vSphere edge deployment, right? And in those scenarios, we just say, hey, this is what I'm going to be. Go now map yourself back in our implementation teams, our customers in some cases are installing these on their own. It's really going to evolve as we can build more and more of that AI into it. And into more of the Dell products as well. That's why I was saying about the workflows earlier because you guys have done the work. And I've been following the work you guys have been doing at VX rail and now Apex Cloud. You guys have done the work because you had to do the workflow on lay that out. Now you have scale and automation and AI coming around the corner. Well, those 277,000 nodes that we've got deployed, we've learned a lot from those customers. What are the typical things that they run into? What are the challenges? How do we automate that out so the next customer doesn't have to hit the same pain point that maybe one has already hit before? How do we improve the life cycle management that we're constantly improving upon? We do something like 800,000 hours of test on every single major release. There's not a lot of customers that can have 800,000 people hours and laptop hours and equipment. That's the benefit of coming with these purpose built systems like VX rail for VMware like our Apex Cloud platforms for Microsoft Azure and also Red Hat OpenShift. I mean, I think AI is going to be a friend to you guys because of the tailwind, because of the existing install base, because of the data, because of the easing up on all that hard heavy lifting on configuration. If software continues to go down the coding route, I mean Shopify's entire headless systems now generated by code we heard down the queue by AI, the glue layers could be filled in by AI. So when you start to think about integrations, this is going to be exciting. It's a lot of fun. Some of the things that we haven't even talked about yet is what you can do with the APIs that we're building in with these. Now you can actually codify the infrastructure, the management of that infrastructure. What happens when AI starts to be utilized in combination with that? Now you don't even need to have necessarily a software developer. You can use AI to give you the first pass at what that API instruction set's going to be. Verify it, make sure it's not hallucinating, and then you can actually have now a faster implementation of new infrastructure along the way, right? I'm laughing because you're basically giving a masterclass, and we have our own AI now, so that's actually on the transcript. That'll be in the AI. And Sienna, you know that my view is I'm pro AI, and I'm anti-AI regulation. So that's my view on AI. John likes to live train our AI while we're sitting here on set. Why don't we train some AI? What is VxRail's strategy for AI? Well, I think you're going to have to wait a little bit more on that. Right now it's making sure that our customers can enable AI workloads in their environments, using GPUs, making sure that they've got the right type of networking readiness for that so that whatever is thrown at them, they're going to be able to run it in their environment with VxRail. I like it. Well, okay, I guess we'll have to be patient, but I'm going to ask you one final question, Bill. Because it's your first time and you've been such a stellar guest. We're certainly going to have you back on the Cube. What do you hope next time we get to sit down together that you can say then that you can't say now? Well, I'd like to say a lot more customers that my son is actually doing some of the things you talked about. All right, Andrew. I think that would be a blast. But I really think that I'd like to see VxRail still in the forefront of the conversation, making life easy for our VMware friends and customers that are trying to deploy AI workloads on vSAN and vSphere. Well, that's absolutely fantastic, Bill. Thank you so much for your wonderful insights. I hope that Andrew is proud watching at home right now. And John, thank you for the great questions, as always. And thank all of you for tuning in to our wall-to-wall live coverage, four days here at Supercomputing 2023 in Denver, Colorado. My name's Savannah Peterson and you're watching the Cube, the leading source for emerging tech news.


Live from Las Vegas, it's theCUBE covering Oracle Modern Customer Experience 2017, brought to you by Oracle. Okay, welcome back everyone, live here at the Mandalay Bay. This is theCUBE's coverage of Oracle's Modern Customer Experience event. I'm John Furrier with SiliconANGLE and my co-host, Peter Burris, with chief researcher at wikibond.com. And our next guest is Nick Edward, who's the president and CEO of Lookbook HQ. Welcome to theCUBE. Thank you very much for having me. We're just talking hockey, so I'm all distracted. I'm disappointed. Good to see you. Before we get into it, we have a lot of intelligent content, some of the things that are going on with this platform. But take a minute to talk about what your company does with some context. Yeah, absolutely. So, we're a marketing technology company based in Toronto. We are a very big part of the Oracle Marketing Cloud app cloud. And we kind of pick up where Oracle basically leaves off, where the marketing cloud stops. So, a lot of what happens in the marketing cloud is focused on generating moments of attention and orchestrating that kind of biased journey. We're what happens on the destination side of the click. So, we focus on the intelligent use of content. How do we deliver content? We think of every moment of attention as core to a marketer. Now, that really is their currency, the attention. We need to actually think that marketers, B to B marketers in particular, need to think a lot more like B to C. I think more like publishers. They're obsessed with attention. We shouldn't be satisfied with clicks or form fills. We need to actually be capitalizing on those amounts of attention to make sure that if Bob is reading my white paper, and I need to know that he's actually reading it, how do I then move him to the next best content asset or give him a choice of content assets in session? So, in essence, what we do, what our company does, and we help companies like Thompson Reuters and ADP. In fact, ADP is speaking on our behalf this afternoon. We have a lot of people, like HollyKom, Quintiles, a whole host of big, electrical customers. What we do is to help them take their content use model from something that looks and feels like blockbuster. One size fits all. I don't know if actually Bob watched the video that he walked out of the shop with all the DVD rather. And hence why Blockbuster is R.A.P. And we take that and make it far more like Netflix. We make it far more on demand. Instrumented. Yeah, very much so. So, this is interesting. The attention to impact is interesting. Intention is essentially aided awareness, which is the holy grail in marketing, right? I mean, getting people to have some aid to a final destination, a transaction of some sort. Am I getting that right? Yeah, very much so. I mean, on the B2B side, obviously, marketers' job is to generate high quality marketing qualified leads. And the real emphasis is on the Q, qualified. But companies like Sirius Decisions report that 94% of marketing qualified leads don't close. And that's a damning statistic for a B2B marketer. And our whole hypothesis, and we're proving this with our customers, the reason why that happens is they're not qualified. Bob might have clicked on an email, he might have filled in a form, et cetera, but did he read the content? We need to get an MQL to engage with five, seven, 10 pieces of content to become a high quality MQL. If we're only doing that one piece of content, every engagement, that's really hard to do. They wonder our sales cycles are slow. Well, we also need to understand that there is a progression that people go through as they learn. It's not just that we want them to click on nine pieces of content, as much as we want to see a pattern. So they've read this content and there was a suggestion made or an option provided and they then took the option, which is an even stronger suggestion that they've absorbed it, they've internalized it, and they're now part of a journey. So how does, I really like the idea that we're on the delivery side of the click. So you've got all the stuff that's happening on the presenting things to options and then you're ensuring that when they click, whatever is being delivered is the highest quality in terms of driving that customer forward in the journey. I got that right? Yeah, absolutely. So if we take something which Alec has done a fantastic job, for example, of teaching the community of their customers about lead nurture. And the typical nurture track looks and feels something like six emails over six weeks, advertising content offer A, then content offer B, content offer C, and typically they're scheduled 10 AM on a Thursday. Now this would be great if we could get Bob and the other 12,000 people that we sent the email to to click on every single one of those emails. And the reality is, if I've got low single digit click through rate, it's not going to happen. So what we do instead is, well, if they're engaging with A, why don't we give them B in the same session? Well, they're here. Well, we've done the hard working and the attention. And to your point, Peter, we're tracking that and then we can start to make some really intelligent decisions going forward as to my word. So this is what resonated with Bob. This content asset's actually performed well. So we have two basic approaches to this. One is, we let the marketer curate that experience, decide what A, B, C is, et cetera. Or we actually use machine learning to auto generate. If Bob arrives at A, what should B, C, and D, et cetera B? So that is very much like Netflix where we kind of base our algorithm. It feels very similar to Netflix's content discovery. That's great for anonymous or net new prospects in the top of your funnel. Once you figure out what they're actually interested in, then you can actually speak for the program. How does it work for you guys? You work hard at registration because content in these days has two flavors, free and gated.  How do you guys view that? Is that part of, or independent of what you do? No, it's a big part of what we do. And one of the things, one of the capabilities that we have in our application is actually the ability to serve forms based on time and behavior. So if they've engaged with three content assets, then maybe actually want them to give me some more information or put up their hands. So we can make the form time-based, you can let them try before you buy, as it were, or you can hard gate it. We use Eloqua forms in our application, so all that information flows as normal if the workflows all get triggered, et cetera. It's very much up to the market of how they want to use it. But one of the things that we increasingly see amongst our customers is the most successful do try to take the forms out of the way. Once they are a member of our known database, how much more information do we really need them to volunteer, particularly with our ability to augment that contact record with other sources of data? Asking the marketer, sorry, asking the prospect for it isn't always the most sensible thing to do. So it's the free versus registration, but it's also new kinds of content. One of the things I like to say is software is content. Yeah, yeah, yeah. Trying software, for example, is content or presenting an interactive experience that has a software element associated with it. Absolutely. It's a crucial part of gauging where people are. Are you able to start embedding your tooling directly into some of these more interactive elements and choose options within that interaction or is it more options on static content? It could be both. So we're fundamentally content diagnostic. The best way to think about what we do is really as a very smart wrapper that goes around the content and then that can be embedded or it can be shared however you want. We use this destination at its own right. So sure, if you want to kick something off with some form of interactive content, absolutely. We also pull all different types of content together. So if your content is distributed or you want to use third party content, reviews, an expert in the space that's writing about something, how do you pull that in and use that as a jumping off point? And what's really interesting is frequently is the sequence of content. That's the most interesting thing, not the behavior engagement of a single asset. Right, and part of the experience that certainly marketing is developing here translates into other disciplines within the business. For example, service. In many respects, one of the things that you're presumably testing is is the prospect learning the right stuff that actually makes them more qualified. Absolutely. Well the same thing can be said for service, self-service, is the person going through the right sequencing. Are you also seeing a demand for this kind of a product over in the service side and is that tying marketing back together? Yeah, that's a great question. And one of the things that, when we kind of officially launched a company in the application three or four years ago now, we focused very much upon demand generation. Like we knew the problem that we were helping them solve but there are a handful of our customers, Cisco being one actually, but actually at the moment all they do is use us on the customer marketing side of things. How do I drive adoption? How do I drive cross-sell and upsell? I mean all this is, we've got to remember that attention is what we're looking for and the way that we achieve that is using content as our primary asset as a marketer. The channels are important, the creative is important but those really are content offers. Bob doesn't buy because he clicks on an email. Bob buys because he reads his stuff and watches the thing. But it's attention and competency. Yeah. So, you know, PsySims used to say, I think PsySims used to say that an informed customer is the best customer. You want a competent customer. That in many respects the process of moving from a marketing qualified and MQL to an SQL is that customer competent enough to actually engage with a sales person or somebody else to do something. That is spot on. So what we're seeing across our customer base is improvement in conversion rates from MQL to SAO, for example. So McAfee, in top security, now McAfee again. They've seen a three times increase in the MQL to SAO conversion rates. Rockwell Automation has actually made a 300X return on their investment in us in nine months by passing higher qualified leads to the sales team. I think they generated $250 million in additional net in pipe. Rockwell, yeah. ADP, that's doing our customer case study this afternoon. A 3X increase in marketing influence opportunities and a 6X increase in closed one marketing influence opportunities. So more, but to your point, Peter, better qualified. We know that these people are actually red our stuff. Therefore, the conversation is easier. They are actually generally qualified. Carrier has been proving that out by actually 2.4 times faster through the funnel to MQL and then their ACV is 2.3 times higher. Why? Because they're not getting the pushback in the sales cycle. The prospect has self-educated and they see the value now. Nick, I want to get your thoughts on something that's involved in our business. We're an independent media company and we don't really have any ads on our site at all. We have a sponsorship model. We have data, but it's interesting. I'm reading an ad age article right now that says for the first time ever, digital has surpassed TV. I'm not going to remember back in the day. It's always this little slice and it's getting bigger and bigger. But for the first time, desktop and mobile ad revenue surpasses TV for the first time, 22% up-spring from the previous year. So digital ads, some are calling it native advertising or whatever that means, is a key part of the attention cycle. So the role that a marketer needs to take with channels is important. And a lot of content marketers are failing these days that we talk to because they're not being authentic with their message and the users can smell, you know, non-relevant content. Some are clever and make it link-bait-ish. And some are actually really super smart and actually do authentic content. But so that's kind of progression. That's an evolution of the industry. But from a data standpoint, there are platforms out there like SiliconANGLE and others that have an opportunity for impression in attention in real time. How does your system, how does your clients, and how do people deal with that? Is there a way, is there mechanisms? So we have two large publisher customers that run multiple different properties and have very large communities that are looking to monetize. And they're all part of the Oracle Marketing Cloud that use their text app. And we're helping them in two ways. Firstly, kind of from an advertising thing, like high value added. Advertising solutions, I guess, is from one of the better description. How do I help to monetize my community, not just pass to HP if they're the advertiser? Here's a list of names of people that build in the form. Here's actually people that are engaging with your content. And it's a mix of our editorial and your content's tell a story. And then one of the things that we're starting to explore with them is actually far more on the native side of things, actually, of being embedded as, essentially, as a native ad in its own right, which can kind of get launched. That's something which I'm keen to explore further. And at the heart of it is probably even a bigger problem on the ad tech side than is on the martech side. But people like Gary Vaynachev are starting to ask the ad tech industry, we need a dose of common sense here, was the marketing consumed? And that's something which I'm fascinated with. We're starting to see that we can actually identify by channels. This channel might, or this particular display provided, DSP may have generated, you say, I don't know, a thousand clicks in the last 30 days. Did it do anything? Like, was it valuable? It was not valuable. Exactly. Yeah, at the end of the day, these moaked, sustained attention, they have to be valuable. I think, John, we're talking really about a continuum from impression to attention to competence. We want to work with competent buyers. Because it cuts down the time that we spend on it, it reduces the risk that we're wasting our time. And quite frankly, it's a lot easier to work with someone who's really engaged and wants to succeed with whatever we're also. He mentioned the publisher angle. I was thinking also from the customer angle, because I'm a customer, a marketer, I'm going to be looking for mechanisms to go to. The publisher wants better monetization of their communities. So have you seen any patterns in the business that could be a use case for helping customers operationalize? And we've had great success with our business in the sense of saying, hey, we're engaging users, so that's good. You should join in with us at the right time, not, you know, try to do it six hours too late. Right? It's like being late to the party, right? Yeah, most definitely. So that real-time piece is really super important. For sure. We've just changed the way that we're integrating with Eloqua to speed that up. So now we've actually moved to using webhooks as part of the integration and using the map form processing capability. It's faster, it's more extensive, it's more scalable. It means we can get very rich in content engagement data into someone's hands faster and better. And I think, what is it, 50% of people buy from the first person that shows up. So being able to do that is critically important. Member-based communities are getting a lot of trends, traction these days. Some call subscribers to firewalls, but member-based. So something we're starting to look at is, how do we actually start to auto-generate the content experience? Yeah, around key accounts or topics, et cetera. Fascinating conversation, Nick, appreciate it coming on. My pleasure. Look, book, HQ, check them out. Doing intelligent content, scaling content, looking at data, congratulating success. Look forward to following up with you on some of the native advertising solutions that we need. Then you need, and congratulations, and Oracle's certainly taking advantage of it. See you next time. Thanks for coming on. We'll be back with more live coverage. I'm John Furrier with Peter Burris after this short break.


Ladies and gentlemen, the railroad hour. And here comes our star-studded show train. The Association of American Railroads presents a memorable musical hit, Victor Herbert's Sweetheart, starring Gordon MacRae and his lovely guests in Zell. Our choir is under the direction of Norman Luboff, and our music is prepared and conducted by Carmen Dragon.  And now, here is our star, Gordon MacRae. Herbert's Sweethearts. All is here beside me to play Sylvia. My name is Franz. The story begins with the bevy of beautiful girls in the fabulous laundry of the White East. And if we finish his laundry, he won't keep coming in to ask for it. Come on, Sylvia, you know my name by this time. Let me see. Your name is right on the edge of my lips. Stand still and I'll take it off. Your laundry isn't ready yet. Good. I'll wait. I'm used to waiting for things. And by the way, how long do you think I'll have to wait for you to fall in love with me? You see, Sylvia, here's the way that I feel. How I miss my native land. The rippling stream. The prints. The magnificent landscape. The prints, prints. Tourists, we can kid. Delaney, let's face it, it's a gopher hope. If you say the word, I can put you back on the throne of Delaney. Just like that. Are you sure my people want me? We'll have an election. I'll count the votes myself. The other royal house died out. Twenty years ago, I mail air. And I left the baby beside this laundry. Now, if there's a pretty girl in there in her early 20s, then I have a stigmatism this good. One was adopted. Air wouldn't shoot. Naturally. Wouldn't shoot. Can't get married. There must be something my father didn't tell me. Air wouldn't shoot. I need to figure out a way to stick around here so I can find out which one is air to the throne of Delaney. Which one do you think it is? The prettiest one, naturally. Now, how are you going to tell which one is the prettiest, Michele? You know, powder and paint makes a girl what she ain't. Who's complaining? Never satisfied. Pressing ruffles in a petticoat. There's papa ironing. I'm not even a papa. I'm crazy. I'm getting sick and tired. Oh, she's got a point there. I confess, I'm not your husband. Then who are you? Well, I'm the guy who left that baby in your tulip garden 20 years ago. Now I want her back. But then Sylvia must be the other air of the throne of Zelenia. Ain't it convenient how these things work out?  Well, I just can't believe it. Hey, kids, your highnesses, listen, when the two of you get restored to the throne of Zelenia, I happen to know of an unemployed prime minister who is between engagements, as wardrobe will travel. Michele, you'll never get a job in my government. Our government, darling. Yes, my sweet, of course. All right, we'll make him third assistant secretary of agriculture in charge of corn. King and queen to get married? No. Let's get married right now. Oh, stop for the ending of a fairy tale. Hans, Christian Andersen, the Grim Brothers, or even Winner, give them cards and spades if they can talk. Whitby Smith and music by Victor Herbert was dramatized for the rare. Thousands of people today are demonstrating just how we get things done in America. For throughout the nation, volunteer workers are pitching in to help the community chest and to make sure assistance will be available to those who need it. So won't you help make your friends and neighbors' jobs just a little easier by contributing generously when they call on you? This year, the expanded community services to the armed forces have been added to the long list of vital and humane red feather agencies. So won't you keep these extra services in mind when you make your contribution to the community chest? All aboard! Well, sir, it looks as though we're ready to pull out. And so until next Monday night, a Nerving Berlin's Holiday Inn with the lovely Dorothy Kirsten, this is Gordon MacCray saying, Remember, Brother Starlin, our choir is under the direction of Norman Luboff and our music is prepared and conducted by Carmen Dragon. This is Marvin Miller saying goodbye until next week for the American Railroads. And now keep tuned for your Monday night music on, man!


Let me define at any time u a of t as, so this is our upper confidence bound based on by Bunstein inequality, right? Bunstein, Hofding's inequality which we have obtained. So, this u j t is simply going to be whatever this quantity we have. So, 2 log t by t j t minus So, when I computed this j t, it is just based on the empirical means. So, j t is, so we after the here I have not written, right? Like how I am going to, so when I said set j t, right? How is this set set j t set? It is just like u a after every in every time t, you will have this i for all i. This is the estimates for each of my arms. That is just simply the empirical average that I am going to take based on that I have taken the first top m arms. These are the remaining arms. Now, this is what I have used to define my j t set and to resolve the conflict between the edge points, I have used my confidence terms, upper and lower confidence terms. This is not just this, sorry. It is actually the mean value plus the confidence term and the lower confidence term is the mean value minus the same low confidence. So, this is the input to you. You can use whatever the confidence term you are going to use and then plug in here, then you are going to get this algorithm. But when I said this algorithm is KLLUCB, this algorithm KLLUCB is going to use a confidence terms which are based on KL divergence that came from other set of concentration inequality that is large deviation principles. These confidence terms came from what? Like as we said Hoffding's inequality. But we said that how good the algorithm depends on how tight these confidence terms are. If the confidence terms are tight, maybe I can also get a better performing algorithm. So, other possibility for let me call this as UCB here. Other possibility for this terms are they are called KL divergence. And what is the confidence term we used in our KLUCB algorithm? It looks something like this, right? Same thing. So, remember this is the confidence terms. This is the indexes. So, this is what we called the UCB indexes, right? Indices. We ranked in the UCB we used only upper confidence bond to rank the all the arms and then played the one with the highest index. In the KLUCB, we use this as the index and we rank the arms based on this value and we play the arm with the highest index. Now, we are just saying when I am going to define this pure exploration algorithm, we are actually using both this upper and lower confidence bonds and we are going to either use this pair of upper and confidence bond or we could use this one which are coming from two different methods. Now, the question is which one is tighter? Is this one is tighter or this one is tighter? So, did Pinska say this? Pinska just said that, right? So, suppose if these are the tighter ones, which confidence, which confidence bonds you like to use? So, naturally you want to use the second one, right? Because if they are going to have a tighter one. So, it can be shown that what we are going to have is this LUCB. So, what we are saying is the upper confidence term we get from KLUCB is dominated by that we could have obtained from the UCB. That means this is a tighter one, right? And also on the lower confidence bond, we are saying that the lower confidence bond we get from the UCB method is or maybe instead of UCB I just call half things. So, this algorithm says the now algorithm complete. So, now I am going to in the KLUCB version of this we are going to use this confidence terms. So, just replace this U and L functions are defined like this at every time and using that you are going to compute this UT and LT functions. Now, once you are able to compute UT, L function, you can compute this BT function and then your algorithm completes. So, as you might have noticed when you are trying to implement compared your UCB against KLUCB, you observe this to be better than the UCB, but you are able to compute this efficiently, this indices. This indices computing it is much easier, right? It is just like empirical estimates and then adding this term. Whereas, this itself is an optimization problem where all of you are able to compute this fast. It took time. It took time, okay, but you optimize the time or it you just let it run, you did minus. So, I mean, you did you notice how much time it reduced before you optimize and after optimize if it was taking before 2 hours after you optimize how much time it took just a few seconds, but binary search how did you set the precision like the precision was good enough for. Yeah, actually you set some 1 or 0.001 10 to the power 7 for that it was converging in few seconds. So, you will face the same difficulty here, but you need to resolve that when you are going to implement this algorithm. Okay, fine. So, this is algorithm actually performs good. We will we are not I am not going to right now give what is its bound, what is its sample complexity, we will discuss it in the next class. So, just take this as an algorithm now. So, we are going to look at another algorithm called Lille UCB. So, LIL stands for here law of. So, I am just going to discuss this algorithm again and we will not discuss much about its sample complexity. Okay, I just want to but I have we have asked you to compare this algorithm with K Lille UCB algorithm and you see that both of have its own issues. By the way, what are the inputs I need it for? What are the tuning parameters in the K Lille UCB algorithms? What are the things you need to tune? Epsilon is the one thing you need to tune. Okay, there is also. So, if I am going to use K Lille UCB there is also this beta sequence right, this is another thing I need to tune. So, how did you tune this beta in the K Lille UCB algorithm before? It was some log log t. So, it is log t plus log log something right. So, what did you do with the log log term? You just made it 0 that is. So, you have to see like that is one tuning parameter you have to set. Had you not set it to 0 but you set it to some other value. So, there was I think if you are right it was log t plus some 3 log log t right. There was a some constant multiplying the log log t. So, you said that constant to be 0 but did you any of you try if instead of setting that constant to be 0, if I just set it 1, did it improve or what is the situation? We did not run. So, you should try this time. Okay, see that if I just going to. So, where you get the best performance. So, do mention what is the value you are going to get for C there. When you submit a report just mention what is that value you are going to see. So, we will see. So, you have to set this parameter epsilon and that is without and you have to set this epsilon without knowing it right. So, this is one hurdle in this algorithm like how to set this appropriately. This is a tuning parameter and another thing is okay, we know that setting that constant to be 0 works better but it is not necessary that every time 0 setting constant is a better maybe some in some cases setting it to be some positive value may work out better and you and I these are anyway but the main tuning parameters are epsilon and this beta. So, see that in your when you implement that if you play with this parameter see if you are going to get a better sample complexity and report that like if I am going to if I when I said this epsilon it work best for this problem setting. Okay. So, keeping that in mind let us see this algorithm what are the things that this algorithm needs to be tuned. What kind of algorithm you like the one with the minimum tuning parameters or with lot of tuning parameters? You want it to be the one with the minimum right like that means your intervention in the algorithm is minimal and these are all supposed to be learning or artificially intelligent algorithm. So, you your interference should be smaller and they should be if at all that a parameter they should be automatically tuning themselves instead of me you manually interfering. Okay, this algorithm it is going to tape if you ask. So, where is the input like I said these are all for a given delta parameters right where did the delta come into picture here. So, the delta is absorbed in this beta functions right how was the delta influencing this beta functions. Okay, so just when you are going to implement check that how this delta where you are going to use this delta and if I am going to change this delta your simplicity is so if you are going to increase your delta what should happen increasing delta means what you are able to tolerate more errors right. That means it should your algorithm should terminate fast. So, play with that and see I think in your question you might have fixed that delta to be something if you are fixed fine but if you want yourself check whether if I am going to change delta anything varies in the algorithm you can just try out that as well. Confidence delta and parameters epsilon lambda is going to take this now initialize. So, the way I define this scale UCB algorithm it is time to identify the top m arms but you can just set m to be equals to 1 in that case it is time to just identify the best term. Okay, so this algorithm I am writing here it is just time to identify the best term okay not the top m arms it is just its focus is to identify the just the best term. So, what this algorithm is doing is it initially samples everybody once and then this is the stopping criteria for this algorithm. So, like here the stopping criteria was whether the BT is going to be epsilon or not. So, here it is going to check so once this condition is going to be violated if there is if for some n i happens to be larger than the some total number of pools of all the others whenever that has happened then this criteria is violated and then it is going to stop otherwise it is going to continue okay. So, when it continues it is going to always play the arm which is again this algorithm defines some UCB index that is coming from some new concentration bound that is called law of iterated logarithm not going to that this is another concentration bounds like based on half rings and whatever k l UCB. So, this they are going to define UCB index like that and what whenever they are going to play it they are going to update the count of that for others no count is updated. So, here it should be n i and then once it so happens that number of pools of a particular arm happens to be larger than the sum of pools of all the other arms then this guy is going to stop and then it is going to output the one which has been pulled maximum number of times at that point as the best term. So, it must be the intuitively it makes stopping criteria makes I if it so happens that one guy has scored so much that it is larger than the sum of rest of the guy scored in the class maybe that guy is best. So, it is just going to use that algorithm but when they do somehow they are going to they exploit this law of iterated algorithm and show that this algorithm stops with finite time and it has a good sample complexity especially I think the result the sample complexity result holds for two arms but you can check how good it is compared to the k l UCB algorithm. Okay, so now there are certain parameters again here to be tuned what are the things to be tuned they are two sigma squares. So, okay another thing they are going to assume that my distributions are sigma sub Gaussian that parameter is that sigma. Okay, but they are also assumed that even though it is sub Gaussian but the means are all in the interval 0 1 the support could be large but the means are restricted to the interval 0 1. Okay, so this is for a sub Gaussian distribution with parameter sigma square. So, everything is defined right. Okay, let us see this let us focus on this lambda. Suppose if I increase lambda do you think it is a sample this stopping criteria is going to meet early or late it is going to meet right like I want the best guy to be much much better than the rest all put together but if I this lambda happens to be smaller maybe it will come down. So, then again this becomes a tuning parameter I mean I cannot like appropriately decide what is the best lambda here right. So, you have to maybe just play with this also they say when you are going to say this guy is going to be superior to this or the other way around see that what are the parameters you need to tune and see that by tuning them. Suppose let us say this guy beat this guy now this guy is a poor chap here right. Let us try to give him some advantage try to tune any of his parameter and see that this guy over takes this guy. Okay, so just like do this I mean it may not be it may happen that one guy beats the other by too much amount but if you see that they are very close. So, that there is not much difference between this guy happens to be beat this guy this sampler complexity turned out to be just 50 here and for this guy turned out to be just 60 I try to tune this parameter and see it can come down by 50 and just some simple manipulations that is just for experiment purposes. Okay, now the two algorithms I said these are kind of already old algorithm the one the KL UCB I said that come in 2013 and this UCB it came in 2014 subsequently people have developed many algorithms. Okay, so if any of you interested to study this pure exploration you can take this as a project topic earlier this was not listed or maybe like you are not aware of this topic right. So because of that if anybody has not taken but you want to look into this you can still take it. Okay, I do not recall all the names but I think there are quite a good few more algorithms that have come which have also some better theoretical guarantees and also I do not know the empirical empirical there are debates some people say it is hard to beat KL UCB says does most of the time but I remember like somebody said there is one more algorithm that has come recently which does sometimes better than this that is open for explorations. So if you one of you are much interested you can do all the comparisons of these algorithms and maybe write a nice report of comparison comparison and doing it a regret job of doing comparing it under different different environment setting. So okay, so let us stop here


Thank you so much, we're going to go ahead and get our panel started today. Again, my name is Lisa Zalinski and I am with the Carnegie Mellon University Libraries. So today we have our open data panel and moderating our panel today is Katie Webster, the Dean of the Carnegie Mellon University Libraries. And with that, I'm actually just going to turn it right over to him. So thank you. Thank you Lisa, good afternoon. Those of you sitting at the back will probably learn fairly quickly that my voice has a habit of going quiet. So I can either keep on shouting or you can move closer to the front and it's your choice. But if I do fade away, either wave your hand or move. Delighted to welcome our panelists who are all leaders in the open data movement in Pittsburgh and beyond. And they represent an array of disciplines from humanities and social sciences to engineering and the natural sciences. Properly sourced and curated, open data arising from scholarly endeavour. They represent a tremendous resource for the research community. And if we get our job right today, that resource can exist for many generations to come. Broke this speaking and I'll repeat that invitation to go to the CFA building on the CMU main campus at 6.30 to learn more about it. But open data from my simplistic perspective is data that anyone can access, use and share, and has a license permitting those activities. Good open data can be leaked to so that it can be easily shared, talked about and it's available in standard and structured format so that others can rely on its traceability, right back to where it originates, can determine the evidence trustworthy and can enhance scholarship into the future. Our four speakers are going to share some of their innovative approaches to create data to make it openly accessible and of the infrastructure requirements they need during their research activity and beyond. I'm going to introduce each of the speakers one after the other and then invite them in turn to say a few words about their activity. We may deviate, it's going to get very informal, white-hearted event. There's lots of food there, there's a bar over there, don't wait till 5.30 to talk up. Just get up, move around. We'll do the same then. Those of you who were at the peer review panel discussion last Monday will have seen some cookies provided by our hosts at the University of Pittsburgh with the open access look. I'm not sure how they did theirs, but ours have been prepared by Lisa and Anne Marie using proper proprietary open access cookie cutters which were printed on the 3D printers on the library yesterday. They are solid things, so 3D printing is sustainable. They are the code to make the room printouts also is available, so if you really have to be art, you can be those 3D printers. So to our panel, and I'll start with Mario Brous' causes to me. So Mario Berger is an associate professor at Carnegie Mellon in the Department of Civil and Environmental Engineering. He's interested in making our built environment more operationally efficient and robust through the use of information and communication technologies so that it can better deal with future resource constraints in a changing environment. He's the faculty co-director of the IBM Smart Infrastructure and Analytics Lab at CMU as well as the director of the Intelligent Infrastructure Research Lab. He's received numerous awards including Dean's Art and Clear Fellowship from CMU earlier this year. He received his BSc in his native Dominican Republic and his master's end doctoral degrees from Carnegie Mellon. Sitting next to Mario Brous' Christopher Warren, an associate professor of English at Carnegie Mellon where he teaches courses on Shakespeare and early modern culture. His research interests include digital humanities, law, literature, political theory, early modern literature, global studies and the history of political thought. His first book, Literature and the Law of Nations, 1580 to 1680, was published earlier this year by Oxford University Press and is a literary history of international law in the age of Shakespeare and Milton and Hall. A digital humanities project he founded with Daniel Shore, the six degrees of Francis Bacon, aims to be the broadest, most accessible source of who knew whom in early modern Britain. The beta site was released earlier this year. Chris will tell you much more about it. He did, in his background notes, tell me that it's been featured in a variety of erudite, scholarly journals such as the Smithsonian Magazine, Mental Foss and for the various Brits that I'm looking at you for the reaction. It has also been featured in The Daily Mail. I've been derived from that, what you wish. But we are very proud of you. Next to Chris is Geoff Hutchison, an associate professor in the chemistry department at Pitt. His research is in materials chemistry, particularly rapid screening of molecules and polymers for engineering, for energy applications. Geoff is here because he was involved in starting the Pitt quantum repository, which can be used for teaching and research. And it's the first database that easily allows students to view 3D molecules on their phone with computed properties of over 100,000 molecules using quantum chemical methods. And they've also developed the open source chemistry program at Fogadro, used by half a million people in 20 languages for a variety of do-it-yourself quantum calculations. Geoff also pointed to the CMU connection in that Professor John Popel, a former CMU faculty member, had a long-term vision of a global quantum chemistry database. And even close at home for me is the fact that John Popel was a Nobel laureate and his Nobel medal is on display just outside of my office. And Bob Credit is the fourth member of our panel. He manages the Western Pennsylvania Regional Data Center at Pitt's University Center for Urban and Social Research. Bob provides overall project management at the data center and also takes the lead in building relationships with data publishers and users. He also is responsible for community engagement efforts. And we've discovered a shared passion for being brutal about the traffic in and around the park of Pittsburgh where we both live. So if you fancy speeding along Farms Avenue, you've got us to deal with. So I am going to turn it over to our panelists and invite Madio to start with some open remarks. So I think you do well put some of the background information about who I am and what I do at CMU. But I'd like to maybe start with more of an anecdote of how I got involved in open data and use that as maybe a starting point so that if you have questions later you can refer back to that. So my quest, I don't know if I would put it as strongly as a quest, but my work in open data started because when I was a master student here I was faced with a question that led to my THD and is still ongoing and is a question about data. Somebody actually was an energy SAR at CMU, that's the actual title. An energy SAR, a person who was in charge of managing energy for all buildings on campus. He came to us, to the masters in the group that I was in and said, we have a problem here at CMU where we only receive a single monthly bill for all our utilities and we don't know where we're spending energy, which building is spending more, which building is spending less, who should we charge, is it the dean of engineering, is it the dean of arts, we don't know. So we took that as a question that we thought could be answered by just measuring. We thought that data could be accessed very easily. And as master students we went on and acquired some instruments and started deploying them around campus to measure electricity consumption at the finest resolution we could find. And that actually eventually led to a conclusion that it is pretty impossible, it's kind of impossible to measure exactly how much you're consuming and know how you're consuming it. And that'll give you one example. So right now you have a single monthly bill, but if I give you information about how much electricity you're consuming, let's say in real time, not just monthly but just in real time, would you be able to make better decisions? So this particular second you're consuming a thousand watts. Next second, a thousand and fifty. Next second, a thousand and a hundred. You don't know what to do with that information. So that data alone wasn't enough. So we started a whole project to figure out if we can translate those measurements into useful information. So we saw a jump of fifty watts in your house. We knew that was exactly a TV. And it couldn't be some other things because we know that TVs jump about fifty watts. And that's, we started to build patterns that we could teach computers to then recognize what was happening in your house and build fingerprints for individual appliances and let computers deal with understanding exactly how much you're consuming when it realigns. And that led to my PhD thesis and everything else. But to solve that question, we need a lot of data. So we don't know what these patterns are, what they look like, and there's no published information about that. So we started a quest to gather all that information and that led to publication from my team and from people around the world on measurements of energy consumption that is annotated and walk you through it so that people can then use all that data to train models and solve it. So I'll stop there, but that's kind of how we got started. Thanks. So as I think the only human is on the panel, I should probably explain that data is not a word that comes naturally to me. In fact, it's only in the last few years, coincident with being at Carnegie Mellon, that I have even sort of thought of myself as being involved in data. And I also sort of want to talk about that story and how that happened. 60 Weeks of Francis Bacon is a project that sort of reconstructs historical social networks, specifically those of people who were alive between 1500 and 1700. And sort of, you know, early on in the kind of genesis of the project, my collaborators and I thought this would be a good idea. But we thought we would go about it in a very traditional humanistic way, which is to say we would look at manuscripts and lists and books and write things down and sort of, you know, turn them into some kind of computer digital artifact, but data was never really part of that sort of conceptualization. But this is a very sort of CMU story. There was a colleague who was hired at CMU when I was in 2010 in the statistics department, and we were sort of walking down the hall. We share a hall in Baker Hall at CMU. And said, hey, do you want to get lunch sometime? Sure. What are you working on? I'm working on that. What are you working on? You know, I'm thinking about reconstructing historical social networks. And this colleague in statistics said, actually, there are some fascinating statistical problems there. And I was blown away. You know, I was blown away. Like, why would statisticians care about historical social networks? You know, I care about great works of art, great works of literature, where people get their ideas, their language. That this is a statistical issue was just kind of incredible to me. And so ultimately we sort of had a series of conversations in which one of his Ph.D. students, at that time the master's student, sort of worked with us a little bit to sort of identify sources of what I soon learned to call unstructured data and to, you know, which I used to call books. And so to sort of identify sources of unstructured data to infer social networks from these sources. And then over time we sort of developed sort of working ways to visualize that data and manipulate that data and learn more and more about the way people were connected in history. And over time I have been convinced about the sort of need for open access in a couple different ways. One way is that when you think about the sources of unstructured data from which you reconstruct historical social networks, one of the kind of key sources for us has been stuff that's already digital in digital form. Ideally HTML or text files. Mostly secondary scholarship. A lot of the secondary scholarship is in the public domain but that's everything published prior to 1923. What that means is that if you're reconstructing social networks from text published before 1923 you are effectively reconstructing the biases and omissions of the Victorian period basically. And so there's a whole kind of century of knowledge more or less that has to be omitted. So, you know, we've been sort of looking at more and more ways to kind of work with works that are in copyright in order to actually have an accurate picture of who knew whom in this period. And the second sort of commitment to open data has to do with what we do with our sort of document matrices and the probability matrices that we developed. You know, if you go to our website right now, 6reesoffrancisbacon.com, you can download all of our data for free and manipulate it and do what you want with it. That's very much a kind of commitment to the recognition that there are a lot of things one could do with this. We do some interesting things with it, I think, but we don't have a sort of monopoly on what you might learn from this data. You know, it's really exciting to me to think about combining this data set with data sets from earlier periods and later periods and be able to kind of say how have social networks changed over time from 1500 to 1800 or from 1500 to 2000. That's a sort of possibility that's only possible if data is open and accessible and that other researchers who are sort of asking sort of parallel or constant questions can use that data and so we want to sort of put it out there and very much the hope is that over time we're going to have, you know, a really rich world of new knowledge developed because historians, literary scholars, historians of science, art historians are thinking about the past in new ways and learning to see how data can help answer questions that are important to humanities. So I'll actually start with a historical side to this, right? So modern scientific methods, scientific thought, really began in the Enlightenment. Before that, particularly in chemistry, like you had alchemists and when they came up with the discovery, they really did not tell anyone, right? They'd write things in various codes, they might not even tell their apprentices and, you know, what transformed science was the Royal Society in starting to publish scientific discovery and philosophically transactions and other scientific journals. And the transition occurred, right, in earlier science and mathematics you'd keep these ideas secret to protect your job to a transition where you'd say, okay, I'm going to tell you all the details so that you can reproduce my discovery. That's how you trust me, and I'm going to publish it and then I'm going to get impact for that. And, right, the fact that we've got hundreds and thousands of scientific journals speaks to the massive success of that. The question that now, right, as we move into a digital realm, let's call it the cheminformatics, right? And that's something I'm actually involved in. How do you process chemical data in new ways to build networks and allow others to build on the raw data files? So when I was a graduate student, I went to Northwestern and John Popol, after he left the CFU went to Northwestern and gave a presentation, and he talked about this idea of a global quantum database. It started because when Popol began in the 70s doing these quantum calculations, they're extremely time consuming. They still are, right? The methods, Moore's law is wonderful but people advance new methods that are more computationally intensive. And so he had this idea, look, why should everyone repeat a calculation of the properties of benzene? Right? Someone should do it once and then publish it in some electronic format that others can access, right? And then, you know, maybe you do, you change things but you can build on it. The problem, of course, is that a lot of chemical software, one of the chemistry methods, are sold. And the company that John Popol created out of CFU actually saw this as a threat to their bottom line, right? Because if you are publishing the results of all the calculations, then you don't need a license to the software program to do all the calculations. So I was really inspired by that presentation that Popol gave and thought, well, look, you know, I had taken plenty of computer science classes. My father had done more computer science and databases, real-time databases and so on. This just sounds like a database problem. It's not going to solve any problem. But as a grad student that sort of sat on the shelf, when I came to Pitt, it was something I was interested in and people said, well, you know, start doing that after tenure. So, okay, fine. You know, I was still interested, as Pete said, right? My group developed this package called Apigadro. It's a user-friendly way on the desktop to draw up molecules and run simulations. I designed this software so that I could take undergrads and high school students and get them to do this kind of research. But the question in Apigadro is, again, the question that John Popol had, well, okay, so I draw out this molecule. You know, if someone's already done a calculation on it, shouldn't I be able to click the button and have it just import the data? Why should I have to do the calculation again? It doesn't make much sense. It's, you know, in 2015. And a colleague came to me and he said, hey, I've got this great idea for education. He said, look, when we teach chemistry, one of the significant problems in teaching chemistry is we draw things in two dimensions on paper on a blackboard in a textbook. But molecules live as 3D objects. And it's a key skill for chemists to be able to take that 2D depiction and then think about it in 3D. And some people do better at this. It's a learned skill. He said, look, every student we have in chemistry class, they all have smartphones. And we're basically telling them, turn off the smartphone and don't work with the user. That seems silly, right? The smartphone is at least as powerful. It's probably thousands of times more powerful than, you know, the computers John Popol was running calculations on in his 70s. And they've got cameras. So, hey, let's put a little of QR barcode or URL on the slide. And they zap the QR code. It takes them to a web page there. They've got the 3D view of the molecule. And I said, oh, this is perfect. You just figured out the access way. But what you need for your method to work is you need a database. You want hundreds of thousands, millions of the most interesting molecules, right? So someone wants to lecture about aspirin. They don't need to go fetch the structure of aspirin, right? It's there at their fingertips. And so Dan and I, we would have a proposal on the Dreyfus Foundation funded it. And that was the genesis of PQR, right? And so on a smartphone or if I actually have a net access on an iPad, right, I can come in and let them be cold, right? Here's molecules. And I can move them around on my screens as three-dimensional objects. And we said, look, this is a great opportunity. So each record in the PQR has a separate, sightable digital object identifier, DOI. We worked with some people in the Pitt library who hooked us up with ways we can mint a few million DOIs without breaking the bank. And we've set them up as living data structures, right? Because we fully expect people to build on them. And we just see that as a continuation of the scientific method in the 21st century. Anybody come up with any good panelist drinking games? I was thinking that maybe I could write a word on here and not let any of the other panelists see it. Anybody wants me to do that? So I come at it maybe from a little bit of a different perspective. I'm out in the community and I'm working with data and I have been for a long time. So I've been at Pitt for six years and before that I spent ten years at Heinz and Carnegie Mellon. And pretty much that whole time I've been helping people either find and use information about the communities in southwestern Pennsylvania. So about ten years ago or so, I collaborated when I was at CMU with folks where I work now at Oxford to create this project called Pittsburgh Neighborhood Community Information System. Anybody heard of it? I don't know why that's surprising. Anybody has. But really it was our goal to try to unlock data from the government. This is pre-open data. We were working with a lot of community organizations. I have a city planning background. So we would get calls from faculty members or community nonprofits working in the middle of the home wood trying to understand who owns this property that we're looking at or is it vacant because we can't really tell or are the owners paying the property taxes. So those were all important questions to community development. We really couldn't answer it unless we had a systematic way to actually get the data out of government and put it together and then share it out with people. And so it was a very manual project. It was like me going to meetings and saying, hey, can I have this data? This is what you want to do with it. And you can trust me because I'm from the university. So, you know, we didn't have the most robust infrastructure. And then once we started to build the relationships within government, everybody was great. But we realized that capacity wasn't there. So people would email me attachments like an Excel file or a CSV file. The data wasn't just a giant mess and I tried with my rudimentary data skills to clean it up. And we did that for a few years. I throw it on the GIS server whenever I have the time to update and the data set was not at all timely but people were pretty appreciative because it was really hard to get data and it still is in so many ways. But, you know, through that work, we became part of this national community. The Urban Institute has a program National Neighborhood Educators Partnership and it's a network of 30 plus cities that really do what we do. We're data intermediaries in the community. We help people outside of academia or even within academia find and use information about their communities. And so what we're trying to do with the new project that I managed, the Western Pennsylvania Regional Data Centers, take that role of being that intermediary, helping people with data, and then connect it to an infrastructure around open data. You know, because we didn't have the legal infrastructure. You know, we had people sign a waiver saying I'm not going to build a responsible university if it's for me to do something with the data. We didn't have any other infrastructure other than that. We didn't have a technological infrastructure other than the GIS server. So it was really hard to really do anything at scale. So after a while, I kind of got a little frustrated with wanting to do more and not having the skills to do it. So kind of sat down with people that we knew in the city and talked to people in other cities and just kind of watched what was going on with open data because it's good to be a last mover in a lot of cases. And just some of the observations that we had, and I'll try to get into what we're doing for some of the moderated questions that Keith is going to have. But the demand for data is growing. It's more and more apparent that people want to use information to solve problems. And in a lot of cases, it's even harder for people to find and use that information. The more data that's out there, it's a real problem. What do I use? Tell me what's important. So those are the kind of things that we try to answer. People also don't want to really go to a website and play with the interface and build the visualization to get data. Our old website, you had to use the map to make your own maps. You couldn't really change the colors on the points and things like that. You can only download records a couple of thousand at a time. So people want to use data on their own terms. They just want it all at once. Download it. API is great if they're going to build a tool. But you've got to give it to them so they can pull it into Tableau or pull it into something else that they want to use. They have great problems encumbering them with any tools that they don't want to touch. So that's another lesson we learned. We also learned, I think this is another lesson that's pretty easy from the story I told, data owners, especially those in the public sector, really don't know how to publish data well. They don't know how to manage it well. And I think there's an opportunity there in what we're doing to really encourage them to document their data and improve the quality of it through a community process and feedback. And those kind of things. So we're really enthusiastic about the response that we're getting from the city of the county, our partners on this project. People also want fresh data, not stale.  More than once or twice a year was just like too painful. I didn't have the funding to really go and hire anybody to do it. I was kind of doing it for a bono. And so, you know, you can see the importance of not having fresh data. I'm like, yeah, if we can only get you this updated list of property owners every month, then you could actually track who owns the, you know, who owns or which investor that's a real problem in the county is buying properties and target them every month. So one of the goals we're trying to do is actually encourage routine automated publishing data. We also learn people don't talk to each other about data. You know, we would learn about things and then we tell other people about things that other people were doing with data that were pretty cool. But those people weren't talking to each other and there was no institutional framework that enabled that to happen. And we're hoping to do some things around that too. Two more. Infrastructure's an afterthought. People just thought about data for their own project and they didn't think about it each other, how other people needed to use the data. And so we're trying to bring an infrastructure perspective to what we do. And it's not just about solving problems, but it's about helping other people solve problems while you solve their own problems. And then the final piece that we learned from our last ten years at work was that problems cross borders. You've got so many issues here, whether it's wastewater issues, vacant abandoned properties, that we've got 130 municipalities in this county, 42 school districts, one county government, how many police forces? I don't know. And then if you go outside of that, you've got even more governments. You've got regional levels of government. You've got authorities. And if you're trying to just solve...


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