>>Mhm Hello and welcome to the cubes coverage of AWS amazon web services, Global public Sector partner awards program. I'm john for your host of the cube here we're gonna talk about the best covid solution to great guests. Benham or with healthcare and life sciences lead at palantir Ben welcome to the cube SAm Michaels, Director of automation and compound management and Cats. National Center for advancing translational sciences and Cats. Part of the NIH National sort of health Gentlemen, thank you for coming on and and congratulations on the best covid solution. >>Thank you so much john >>so I gotta, I gotta ask you the best solution is when can I get the vaccine? How fast how long it's gonna last but I really appreciate you guys coming on. I >>hope you're vaccinated. I would say john that's outside of our hands. I would say if you've not got vaccinated, go get vaccinated right now, have someone stab you in the arm, you know, do not wait and and go for it. That's not on us. But you got that >>opportunity that we have that done. I got to get on a plane and all kinds of hoops to jump through. We need a better solution anyway. You guys have a great technical so I wanna I wanna dig in all seriousness aside getting inside. Um you guys have put together a killer solution that really requires a lot of data can let's step back and and talk about first. What was the solution that won the award? You guys have a quick second set the table for what we're talking about. Then we'll start with you. >>So the national covered cohort collaborative is a secure data enclave putting together the HR records from more than 60 different academic medical centers across the country and they're making it available to researchers to, you know, ask many and varied questions to try and understand this disease better. >>See and take us through the challenges here. What was going on? What was the hard problem? I'll see everyone had a situation with Covid where people broke through and cloud as he drove it amazon is part of the awards, but you guys are solving something. What was the problem statement that you guys are going after? What happened? >>I I think the problem statement is essentially that, you know, the nation has the electronic health records, but it's very fragmented, right. You know, it's been is highlighted is there's there's multiple systems around the country, you know, thousands of folks that have E H. R. S. But there is no way from a research perspective to actually have access in any unified location. And so really what we were looking for is how can we essentially provide a centralized location to study electronic health records. But in a Federated sense because we recognize that the data exist in other locations and so we had to figure out for a vast quantity of data, how can we get data from those 60 sites, 60 plus that Ben is referencing from their respective locations and then into one central repository, but also in a common format. Because that's another huge aspect of the technical challenge was there's multiple formats for electronic health records, there's different standards, there's different versions. And how do you actually have all of this data harmonised into something which is usable again for research? >>Just so many things that are jumping in my head right now, I want to unpack one at the time Covid hit the scramble and the imperative for getting answers quickly was huge. So it's a data problem at a massive scale public health impact. Again, we were talking before we came on camera, public health records are dirty, they're not clean. A lot of things are weird. I mean, just just massive amount of weird problems. How did you guys pull together take me through how this gets done? What what happened? Take us through the the steps He just got together and said, let's do this. How does it all happen? >>Yeah, it's a great and so john, I would say so. Part of this started actually several years ago. I explain this when people talk about in three C is that and Cats has actually established what we like to call, We support a program which is called the Clinical translation Science Award program is the largest single grant program in all of NIH. And it constitutes the bulk of the Cats budget. So this is extra metal grants which goes all over the country. And we wanted this group to essentially have a common research environment. So we try to create what we call the secure scientific collaborative platforms. Another example of this is when we call the rare disease clinical research network, which again is a consortium of 20 different sites around the nation. And so really we started working this several years ago that if we want to Build an environment that's collaborative for researchers around the country around the world, the natural place to do that is really with a cloud first strategy and we recognize this as and cats were about 600 people now. But if you look at the size of our actual research community with our grantees were in the thousands. And so from the perspective that we took several years ago was we have to really take a step back. And if we want to have a comprehensive and cohesive package or solution to treat this is really a mid sized business, you know, and so that means we have to treat this as a cloud based enterprise. And so in cats several years ago had really gone on this strategy to bring in different commercial partners, of which one of them is Palin tear. It actually started with our intramural research program and obviously very heavy cloud use with AWS. We use your we use google workspace, essentially use different cloud tools to enable our collaborative researchers. The next step is we also had a project. If we want to have an environment, we have to have access. And this is something that we took early steps on years prior that there is no good building environment if people can't get in the front door. So we invested heavily and create an application which we call our Federated authentication system. We call it unified and cats off. So we call it, you know, for short and and this is the open source in house project that we built it and cats. And we wanted to actually use this for all sorts of implementation, acting as the front door to this collaborative environment being one of them. And then also by by really this this this interest in electronic health records that had existed prior to the Covid pandemic. And so we've done some prior work via mixture of internal investments in grants with collaborative partners to really look at what it would take to harmonize this data at scale. And so like you mentioned, Covid hit it. Hit really hard. Everyone was scrambling for answers. And I think we had a bit of these pieces um, in play. And then that's I think when we turned to ban and the team at volunteer and we said we have these components, we have these pieces what we really need. Something independent that we can stand up quickly to really address some of these problems. One of the biggest one being that data ingestion and the harmonization step. And so I can let Ben really speak to that one. >>Yeah. Ben Library because you're solving a lot of collaboration problems, not just the technical problem but ingestion and harmonization ingestion. Most people can understand is that the data warehousing or in the database know that what that means? Take us through harmonization because not to put a little bit of shade on this, but most people think about, you know, these kinds of research or non profits as a slow moving, you know, standing stuff up sandwich saying it takes time you break it down. By the time you you didn't think things are over. This was agile. So take us through what made it an agile because that's not normal. I mean that's not what you see normally. It's like, hey we'll see you next year. We stand that up. Yeah. At the data center. >>Yeah, I mean so as as Sam described this sort of the question of data on interoperability is a really essential problem for working with this kind of data. And I think, you know, we have data coming from more than 60 different sites and one of the reasons were able to move quickly was because rather than saying oh well you have to provide the data in a certain format, a certain standard. Um and three C. was able to say actually just give us the data how you have it in whatever format is easiest for you and we will take care of that process of actually transforming it into a single standard data model, converting all of the medical vocabularies, doing all of the data quality assessment that's needed to ensure that data is actually ready for research and that was very much a collaborative endeavor. It was run out of a team based at johns Hopkins University, but in collaboration with a broad range of researchers who are all adding their expertise and what we were able to do was to provide the sort of the technical infrastructure for taking the transformation pipelines that are being developed, that the actual logic and the code and developing these very robust kind of centralist templates for that. Um, that could be deployed just like software is deployed, have changed management, have upgrades and downgrades and version control and change logs so that we can roll that out across a large number of sites in a very robust way very quickly. So that's sort of that, that that's one aspect of it. And then there was a bunch of really interesting challenges along the way that again, a very broad collaborative team of researchers worked on and an example of that would be unit harmonization and inference. So really simple things like when a lab result arrives, we talked about data quality, um, you were expected to have a unit right? Like if you're reporting somebody's weight, you probably want to know if it's in kilograms or pounds, but we found that a very significant proportion of the time the unit was actually missing in the HR record. And so unless you can actually get that back, that becomes useless. And so an approach was developed because we had data across 60 or more different sites, you have a large number of lab tests that do have the correct units and you can look at the data distributions and decide how likely is it that this missing unit is actually kilograms or pounds and save a huge portion of these labs. So that's just an example of something that has enabled research to happen that would not otherwise have been able >>just not to dig in and rat hole on that one point. But what time saving do you think that saves? I mean, I can imagine it's on the data cleaning side. That's just a massive time savings just in for Okay. Based on the data sampling, this is kilograms or pounds. >>Exactly. So we're talking there's more than 3.5 billion lab records in this data base now. So if you were trying to do this manually, I mean, it would take, it would take to thousands of years, you know, it just wouldn't be a black, it would >>be a black hole in the dataset, essentially because there's no way it would get done. Ok. Ok. Sam take me through like from a research standpoint, this normalization, harmonization the process. What does that enable for the, for the research and who decides what's the standard format? So, because again, I'm just in my mind thinking how hard this is. And then what was the, what was decided? Was it just on the base records what standards were happening? What's the impact of researchers >>now? It's a great quite well, a couple things I'll say. And Ben has touched on this is the other real core piece of N three C is the community, right? You know, And so I think there's a couple of things you mentioned with this, johN is the way we execute this is, it was very nimble, it was very agile and there's something to be said on that piece from a procurement perspective, the government had many covid authorities that were granted to make very fast decisions to get things procured quickly. And we were able to turn this around with our acquisition shop, which we would otherwise, you know, be dead in the water like you said, wait a year ago through a normal acquisition process, which can take time, but that's only one half the other half. And really, you're touching on this and Ben is touching on this is when he mentions the research as we have this entire courts entire, you know, research community numbering in the thousands from a volunteer perspective. I think it's really fascinating. This is a really a great example to me of this public private partnership between the companies we use, but also the academic participants that are actually make up the community. Um again, who the amount of time they have dedicated on this is just incredible. So, so really, what's also been established with this is core governance. And so, you know, you think from assistance perspective is, you know, the Palin tear this environment, the N three C environment belongs to the government, but the N 33 the entire actually, you know, program, I would say, belongs to the community. We have co governance on this. So who decides really is just a mixture between the folks on End Cats, but not just end cast as folks at End Cats, folks that, you know, and I proper, but also folks and other government agencies, but also the, the academic communities and entire these mixed governance teams that actually set the stage for all of this. And again, you know, who's gonna decide the standard, We decide we're gonna do this in Oman 5.3 point one um is the standard we're going to utilize. And then once the data is there, this is what gets exciting is then they have the different domain teams where they can ask different research questions depending upon what has interest scientifically to them. Um and so really, you know, we viewed this from the government's perspective is how do we build again the secure platform where we can enable the research, but we don't really want to dictate the research. I mean, the one criteria we did put your research has to be covid focused because very clearly in response to covid, so you have to have a Covid focus and then we have data use agreements, data use request. You know, we have entire governance committees that decide is this research in scope, but we don't want to dictate the research types that the domain teams are bringing to the table. >>And I think the National Institutes of Health, you think about just that their mission is to serve the public health. And I think this is a great example of when you enable data to be surfaced and available that you can really allow people to be empowered and not to use the cliche citizen analysts. But in a way this is what the community is doing. You're doing research and allowing people from volunteers to academics to students to just be part of it. That is citizen analysis that you got citizen journalism. You've got citizen and uh, research, you've got a lot of democratization happening here. Is that part of it was a result of >>this? Uh, it's both. It's a great question. I think it's both. And it's it's really by design because again, we want to enable and there's a couple of things that I really, you know, we we clamor with at end cats. I think NIH is going with this direction to is we believe firmly in open science, we believe firmly in open standards and how we can actually enable these standards to promote this open science because it's actually nontrivial. We've had, you know, the citizen scientists actually on the tricky problem from a governance perspective or we have the case where we actually had to have students that wanted access to the environment. Well, we actually had to have someone because, you know, they have to have an institution that they come in with, but we've actually across some of those bridges to actually get students and researchers into this environment very much by design, but also the spirit which was held enabled by the community, which, again, so I think they go they go hand in hand. I planned for >>open science as a huge wave, I'm a big fan, I think that's got a lot of headroom because open source, what that's done to software, the software industry, it's amazing. And I think your Federated idea comes in here and Ben if you guys can just talk through the Federated, because I think that might enable and remove some of the structural blockers that might be out there in terms of, oh, you gotta be affiliate with this or that our friends got to invite you, but then you got privacy access and this Federated ID not an easy thing, it's easy to say. But how do you tie that together? Because you want to enable frictionless ability to come in and contribute same time you want to have some policies around who's in and who's not. >>Yes, totally, I mean so Sam sort of already described the the UNa system which is the authentication system that encounters has developed. And obviously you know from our perspective, you know we integrate with that is using all of the standard kind of authentication protocols and it's very easy to integrate that into the family platform um and make it so that we can authenticate people correctly. But then if you go beyond authentication you also then to actually you need to have the access controls in place to say yes I know who this person is, but now what should they actually be able to see? Um And I think one of the really great things in Free C has done is to be very rigorous about that. They have their governance rules that says you should be using the data for a certain purpose. You must go through a procedure so that the access committee approves that purpose. And then we need to make sure that you're actually doing the work that you said you were going to. And so before you can get your data back out of the system where your results out, you actually have to prove that those results are in line with the original stated purpose and the infrastructure around that and having the access controls and the governance processes, all working together in a seamless way so that it doesn't, as you say, increase the friction on the researcher and they can get access to the data for that appropriate purpose. That was a big component of what we've been building out with them three C. Absolutely. >>And really in line john with what NIH is doing with the research, all service, they call this raz. And I think things that we believe in their standards that were starting to follow and work with them closely. Multifactor authentication because of the point Ben is making and you raised as well, you know, one you need to authenticate, okay. This you are who you say you are. And and we're recognizing that and you're, you know, the author and peace within the authors. E what do you authorized to see? What do you have authorization to? And they go hand in hand and again, non trivial problems. And especially, you know, when we basis typically a lot of what we're using is is we'll do direct integrations with our package. We using commons for Federated access were also even using login dot gov. Um, you know, again because we need to make sure that people had a means, you know, and login dot gov is essentially a runoff right? If they don't have, you know an organization which we have in common or a Federated access to generate a login dot gov account but they still are whole, you know beholden to the multi factor authentication step and then they still have to get the same authorizations because we really do believe access to these environment seamlessly is absolutely critical, you know, who are users are but again not make it restrictive and not make it this this friction filled process. That's very that's very >>different. I mean you think about nontrivial, totally agree with you and if you think about like if you were in a classic enterprise, I thought about an I. T. Problem like bring your own device to work and that's basically what the whole world does these days. So like you're thinking about access, you don't know who's coming in, you don't know where they're coming in from, um when the churn is so high, you don't know, I mean all this is happening, right? So you have to be prepared two Provisions and provide resource to a very lightweight access edge. >>That's right. And that's why it gets back to what we mentioned is we were taking a step back and thinking about this problem, you know, an M three C became the use case was this is an enterprise I. T. Problem. Right. You know, we have users from around the world that want to access this environment and again we try to hit a really difficult mark, which is secure but collaborative, Right? That's that's not easy, you know? But but again, the only place this environment could take place isn't a cloud based environment, right? Let's be real. You know, 10 years ago. Forget it. You know, Again, maybe it would have been difficult, but now it's just incredible how much they advanced that these real virtual research organizations can start to exist and they become the real partnerships. >>Well, I want to Well, that's a great point. I want to highlight and call out because I've done a lot of these interviews with awards programs over the years and certainly in public sector and open source over many, many years. One of the things open source allows us the code re use and also when you start getting in these situations where, okay, you have a crisis covid other things happen, nonprofits go, that's the same thing. They, they lose their funding and all the code disappears. Saying with these covid when it becomes over, you don't want to lose the momentum. So this whole idea of re use this platform is aged deplatforming of and re factoring if you will, these are two concepts with a cloud enables SAM, I'd love to get your thoughts on this because it doesn't go away when Covid's >>over, research still >>continues. So this whole idea of re platform NG and then re factoring is very much a new concept versus the old days of okay, projects over, move on to the next one. >>No, you're absolutely right. And I think what first drove us is we're taking a step back and and cats, you know, how do we ensure that sustainability? Right, Because my background is actually engineering. So I think about, you know, you want to build things to last and what you just described, johN is that, you know, that, that funding, it peaks, it goes up and then it wanes away and it goes and what you're left with essentially is nothing, you know, it's okay you did this investment in a body of work and it goes away. And really, I think what we're really building are these sustainable platforms that we will actually grow and evolve based upon the research needs over time. And I think that was really a huge investment that both, you know, again and and Cats is made. But NIH is going in a very similar direction. There's a substantial investment, um, you know, made in these, these these these really impressive environments. How do we make sure the sustainable for the long term? You know, again, we just went through this with Covid, but what's gonna come next? You know, one of the research questions that we need to answer, but also open source is an incredibly important piece of this. I think Ben can speak this in a second, all the harmonization work, all that effort, you know, essentially this massive, complex GTL process Is in the N three Seagate hub. So we believe, you know, completely and the open source model a little bit of a flavor on it too though, because, you know, again, back to the sustainability, john, I believe, you know, there's a room for this, this marriage between commercial platforms and open source software and we need both. You know, as we're strong proponents of N cats are both, but especially with sustainability, especially I think Enterprise I. T. You know, you have to have professional grade products that was part of, I would say an experiment we ran out and cast our thought was we can fund academic groups and we can have them do open source projects and you'll get some decent results. But I think the nature of it and the nature of these environments become so complex. The experiment we're taking is we're going to provide commercial grade tools For the academic community and the researchers and let them use them and see how they can be enabled and actually focus on research questions. And I think, you know, N3C, which we've been very successful with that model while still really adhering to the open source spirit and >>principles as an amazing story, congratulated, you know what? That's so awesome because that's the future. And I think you're onto something huge. Great point, Ben, you want to chime in on this whole sustainability because the public private partnership idea is the now the new model innovation formula is about open and collaborative. What's your thoughts? >>Absolutely. And I mean, we uh, volunteer have been huge proponents of reproducibility and openness, um in analyses and in science. And so everything done within the family platform is done in open source languages like python and R. And sequel, um and is exposed via open A. P. I. S and through get repository. So that as SaM says, we've we've pushed all of that E. T. L. Code that was developed within the platform out to the cats get hub. Um and the analysis code itself being written in those various different languages can also sort of easily be pulled out um and made available for other researchers in the future. And I think what we've also seen is that within the data enclave there's been an enormous amount of re use across the different research projects. And so actually having that security in place and making it secure so that people can actually start to share with each other securely as well. And and and be very clear that although I'm sharing this, it's still within the range of the government's requirements has meant that the, the research has really been accelerated because people have been able to build and stand on the shoulders of what earlier projects have done. >>Okay. Ben. Great stuff. 1000 researchers. Open source code and get a job. Where do I sign up? I want to get involved. This is amazing. Like it sounds like a great party. >>We'll send you a link if you do a search on on N three C, you know, do do a search on that and you'll actually will come up with a website hosted by the academic side and I'll show you all the information of how you can actually connect and john you're welcome to come in. Billion by all means >>billions of rows of data being solved. Great tech he's working on again. This is a great example of large scale the modern era of solving problems is here. It's out in the open, Open Science. Sam. Congratulations on your great success. Ben Award winners. You guys doing a great job. Great story. Thanks for sharing here with us in the queue. Appreciate it. >>Thank you, john. >>Thanks for having us. >>Okay. It is. Global public sector partner rewards best Covid solution palantir and and cats. Great solution. Great story. I'm john Kerry with the cube. Thanks for watching. Mm mm. >>Mhm

>> Hello everyone. On today's panel, the theme is Drug Discovery and how Artificial Intelligence can make a difference. On the panel today, we are honored to have Dr. Ryan Yates, principal scientist at The National Center for Natural Products Research, with a focus on botanicals specifically the pharmacokinetics, which is essentially how the drug changes over time in our body and pharmacodynamics which is essentially how drugs affects our body. And of particular interest to him is the use of AI in preclinical screening models to identify chemical combinations that can target chronic inflammatory processes such as fatty liver disease, cognitive impairment and aging. Welcome, Ryan. Thank you for coming. >> Good morning. Thank you for having me. >> The other distinguished panelist is Dr. Rangan Sukumar, our very own, is a distinguished technologist at the CTO office for High Performance Computing and Artificial Intelligence with a PHD in AI and 70 publications that can be applied in drug discovery, autonomous vehicles and social network analysis. Hey Rangan, welcome. Thank you for coming, by sparing the time. We have also our distinguished Chris Davidson. He is leader of our HPC and AI Application and Performance Engineering team. His job is to tune and benchmark applications, particularly in the applications of weather, energy, financial services and life sciences. Yes so particular interest is life sciences he spent 10 years in biotech and medical diagnostics. Hi Chris, welcome. Thank you for coming. >> Nice to see you. >> Well let's start with your Chris, yes, you're regularly interfaced with pharmaceutical companies and worked also on the COVID-19 White House Consortium. You know tell us, let's kick this off and tell us a little bit about your engagement in the drug discovery process. >> Right and that's a good question I think really setting the framework for what we're talking about here is to understand what is the drug discovery process. And that can be kind of broken down into I would say four different areas, there's the research and development space, the preclinical studies space, clinical trial and regulatory review. And if you're lucky, hopefully approval. Traditionally this is a slow arduous process it costs a lot of money and there's a high amount of error. Right, however this process by its very nature is highly iterate and has just huge amounts of data, right it's very data intensive, right and it's these characteristics that make this process a great target for kind of new approaches in different ways of doing things. Right, so for the sake of discussion, right, go ahead. >> Oh yes, so you mentioned data intensive brings to mind Artificial Intelligence, you know, so Artificial Intelligence making the difference here in this process, is that so? >> Right, and some of those novel approaches are actually based on Artificial Intelligence whether it's deep learning and machine learning, et cetera, you know, prime example would say, let's just say for the sake of discussion, let's say there's a brand new virus, causes flu-like symptoms, shall not be named if we focus kind of on the R and D phase, right our goal is really to identify target for the treatment and then screen compounds against it see which, you know, which ones we take forward right to this end, technologies like cryo-electron, cryogenic electron microscopy, just a form of microscopy can provide us a near atomic biomolecular map of the samples that we're studying, right whether that's a virus, a microbe, the cell that it's attaching to and so on, right AI, for instance, has been used in the particle picking aspect of this process. When you take all these images, you know, there are only certain particles that we want to take and study, right whether they have good resolution or not whether it's in the field of the frame and image recognition is a huge part of this, it's massive amounts of data in AI can be very easily, you know, used to approach that. Right, so with docking, you can take the biomolecular maps that you achieved from cryo-electron microscopy and you can take those and input that into the docking application and then run multiple iterations to figure out which will give you the best fit. AI again, right, this is iterative process it's extremely data intensive, it's an easy way to just apply AI and get that best fit doing something in a very, you know, analog manner that would just take humans very long time to do or traditional computing a very long time to do. >> Oh, Ryan, Ryan, you work at the NCNPR, you know, very exciting, you know after all, you know, at some point in history just about all drugs were from natural products yeah, so it's great to have you here today. Please tell us a little bit about your work with the pharmaceutical companies, especially when it is often that drug cocktails or what they call Polypharmacology, is the answer to complete drug therapy. Please tell us a bit more with your work there. >> Yeah thank you again for having me here this morning Dr. Goh, it's a pleasure to be here and as you said, I'm from the National Center for Natural Products Research you'll hear me refer to it as the NCNPR here in Oxford, Mississippi on the Ole Miss Campus, beautiful setting here in the South and so, what, as you said historically, what the drug discovery process has been, and it's really not a drug discovery process is really a therapy process, traditional medicine is we've looked at natural products from medicinal plants okay, in these extracts and so where I'd like to begin is really sort of talking about the assets that we have here at the NCNPR one of those prime assets, unique assets is our medicinal plant repository which comprises approximately 15,000 different medicinal plants. And what that allows us to do, right is to screen mine, that repository for activities so whether you have a disease of interest or whether you have a target of interest then you can use this medicinal plant repository to look for actives, in this case active plants. It's really important in today's environment of drug discovery to really understand what are the actives in these different medicinal plants which leads me to the second unique asset here at the NCNPR and that is our what I'll call a plant deconstruction laboratory so without going into great detail, but what that allows us to do is through a how to put workstation, right, is to facilitate rapid isolation and identification of phytochemicals in these different medicinal plants right, and so things that have historically taken us weeks and sometimes months, think acetylsalicylic acid from salicylic acid as a pain reliever in the willow bark or Taxol, right as an anti-cancer drug, right now we can do that with this system on the matter of days or weeks so now we're talking about activity from a plant and extract down to phytochemical characterization on a timescale, which starts to make sense in modern drug discovery, alright and so now if you look at these phytochemicals, right, and you ask yourself, well sort of who is interested in that and why, right what are traditional pharmaceutical companies, right which I've been working with for 20, over 25 years now, right, typically uses these natural products where historically has used these natural products as starting points for new drugs. Right, so in other words, take this phytochemical and make chemicals synthetic modifications in order to achieve a potential drug. But in the context of natural products, unlike the pharmaceutical realm, there is often times a big knowledge gap between a disease and a plant in other words I have a plant that has activity, but how to connect those dots has been really laborious time consuming so it took us probably 50 years to go from salicylic acid and willow bark to synthesize acetylsalicylic acid or aspirin it just doesn't work in today's environment. So casting about trying to figure out how we expedite that process that's when about four years ago, I read a really fascinating article in the Los Angeles Times about my colleague and business partner, Dr. Rangan Sukumar, describing all the interesting things that he was doing in the area of Artificial Intelligence. And one of my favorite parts of this story is basically, unannounced, I arrived at his doorstep in Oak Ridge, he was working Oak Ridge National Labs at the time, and I introduced myself to him didn't know what was coming, didn't know who I was, right and I said, hey, you don't know me you don't know why I'm here, I said, but let me tell you what I want to do with your system, right and so that kicked off a very fruitful collaboration and friendship over the last four years using Artificial Intelligence and it's culminated most recently in our COVID-19 project collaborative research between the NCNPR and HP in this case. >> From what I can understand also as Chris has mentioned highly iterative, especially with these combination mixture of chemicals right, in plants that could affect a disease. We need to put in effort to figure out what are the active components in that, that affects it yeah, the combination and given the layman's way of understanding it you know and therefore iterative and highly data intensive. And I can see why Rangan can play a huge significant role here, Rangan, thank you for joining us So it's just a nice segue to bring you in here, you know, given your work with Ryan over so many years now, tell I think I'm also quite interested in knowing a little about how it developed the first time you met and the process and the things you all work together on that culminated into the progress at the advanced level today. Please tell us a little bit about that history and also the current work. Rangan. >> So, Ryan, like he mentioned, walked into my office about four years ago and he was like hey, I'm working on this Omega-3 fatty acid, what can your system tell me about this Omega-3 fatty acid and I didn't even know how to spell Omega-3 fatty acids that's the disconnect between the technologist and the pharmacologist, they have terms of their own right since then we've come a long way I think I understand his terminologies now and he understands that I throw words like knowledge graphs and page rank and then all kinds of weird stuff that he's probably never heard in his life before right, so it's been on my mind off to different domains and terminologies in trying to accept each other's expertise in trying to work together on a collaborative project. I think the core of what Ryan's work and collaboration has led me to understanding is what happens with the drug discovery process, right so when we think about the discovery itself, we're looking at companies that are trying to accelerate the process to market, right an average drug is taking 12 years to get to market the process that Chris just mentioned, Right and so companies are trying to adopt what's called the in silico simulation techniques and in silico modeling techniques into what was predominantly an in vitro, in silico, in vivo environment, right. And so the in silico techniques could include things like molecular docking, could include Artificial Intelligence, could include other data-driven discovery methods and so forth, and the essential component of all the things that you know the discovery workflows have is the ability to augment human experts to do the best by assisting them with what computers do really really well. So, in terms of what we've done as examples is Ryan walks in and he's asking me a bunch of questions and few that come to mind immediately, the first few are, hey, you are an Artificial Intelligence expert can you sift through a database of molecules the 15,000 compounds that he described to prioritize a few for next lab experiments? So that's question number one. And he's come back into my office and asked me about hey, there's 30 million publications in PubMag and I don't have the time to read everything can you create an Artificial Intelligence system that once I've picked these few molecules will tell me everything about the molecule or everything about the virus, the unknown virus that shows up, right. Just trying to understand what are some ways in which he can augment his expertise, right. And then the third question, I think he described better than I'm going to was how can technology connect these dots. And typically it's not that the answer to a drug discovery problem sits in one database, right he probably has to think about uniproduct protein he has to think about phytochemical, chemical or informatics properties, data and so forth. Then he talked about the phytochemical interaction that's probably in another database. So when he is trying to answer other question and specifically in the context of an unknown virus that showed up in late last year, the question was, hey, do we know what happened in this particular virus compared to all the previous viruses? Do we know of any substructure that was studied or a different disease that's part of this unknown virus and can I use that information to go mine these databases to find out if these interactions can actually be used as a repurpose saying, hook, say this drug does not interact with this subsequence of a known virus that also seems to be part of this new virus, right? So to be able to connect that dot I think the abstraction that we are learning from working with pharma companies is that this drug discovery process is complex, it's iterative, and it's a sequence of needle in the haystack search problems, right and so one day, Ryan would be like, hey, I need to match genome, I need to match protein sequences between two different viruses. Another day it would be like, you know, I need to sift through a database of potential compounds, identified side effects and whatnot other day it could be, hey, I need to design a new molecule that never existed in the world before I'll figure out how to synthesize it later on, but I need a completely new molecule because of patentability reasons, right so it goes through the entire spectrum. And I think where HP has differentiated multiple times even the recent weeks is that the technology infusion into drug discovery, leads to several aha! Moments. And, aha moments typically happened in the other few seconds, and not the hours, days, months that Ryan has to laboriously work through. And what we've learned is pharma researchers love their aha moments and it leads to a sound valid, well founded hypothesis. Isn't that true Ryan? >> Absolutely. Absolutely. >> Yeah, at some point I would like to have a look at your, peak the list of your aha moments, yeah perhaps there's something quite interesting in there for other industries too, but we'll do it at another time. Chris, you know, with your regular work with pharmaceutical companies especially the big pharmas, right, do you see botanicals, coming, being talked about more and more there? >> Yeah, we do, right. Looking at kind of biosimilars and drugs that are already really in existence is kind of an important point and Dr. Yates and Rangan, with your work with databases this is something important to bring up and much of the drug discovery in today's world, isn't from going out and finding a brand new molecule per se. It's really looking at all the different databases, right all the different compounds that already exist and sifting through those, right of course data is mind, and it is gold essentially, right so a lot of companies don't want to share their data. A lot of those botanicals data sets are actually open to the public to use in many cases and people are wanting to have more collaborative efforts around those databases so that's really interesting to kind of see that being picked up more and more. >> Mm, well and Ryan that's where NCNPR hosts much of those datasets, yeah right and it's interesting to me, right you know, you were describing the traditional way of drug discovery where you have a target and a compound, right that can affect that target, very very specific. But from a botanical point of view, you really say for example, I have an extract from a plant that has combination of chemicals and somehow you know, it affects this disease but then you have to reverse engineer what those chemicals are and what the active ones are. Is that very much the issue, the work that has to be put in for botanicals in this area? >> Yes Doctor Goh, you hit it exactly. >> Now I can understand why a highly iterative intensive and data intensive, and perhaps that's why Rangan, you're highly valuable here, right. So tell us about the challenge, right the many to many intersection to try and find what the targets are, right given these botanicals that seem to affect the disease here what methods do you use, right in AI, to help with this? >> Fantastic question, I'm going to go a little bit deeper and speak like Ryan in terminology, but here we go. So with going back to about starting of our conversation right, so let's say we have a database of molecules on one side, and then we've got the database of potential targets in a particular, could be a virus, could be bacteria, could be whatever, a disease target that you've identified, right >> Oh this process so, for example, on a virus, you can have a number of targets on the virus itself some have the spike protein, some have the other proteins on the surface so there are about three different targets and others on a virus itself, yeah so a lot of people focus on the spike protein, right but there are other targets too on that virus, correct? >> That is exactly right. So for example, so the work that we did with Ryan we realized that, you know, COVID-19 protein sequence has an overlap, a significant overlap with previous SARS-CoV-1 virus, not only that, but it overlap with MERS, that's overlapped with some bad coronavirus that was studied before and so forth, right so knowing that and it's actually broken down into multiple and Ryan I'm going to steal your words, non-structural proteins, envelope proteins, S proteins, there's a whole substructure that you can associate an amino acid sequence with, right so on the one hand, you have different targets and again, since we did the work it's 160 different targets even on the COVID-19 mark, right and so you find a match, that we say around 36, 37 million molecules that are potentially synthesizable and try to figure it out which one of those or which few of those is actually going to be mapping to which one of these targets and actually have a mechanism of action that Ryan's looking for, that'll inhibit the symptoms on a human body, right so that's the challenge there. And so I think the techniques that we can unrule go back to how much do we know about the target and how much do we know about the molecule, alright. And if you start off a problem with I don't know anything about the molecule and I don't know anything about the target, you go with the traditional approaches of docking and molecular dynamics simulations and whatnot, right. But then, you've done so much docking before on the same database for different targets, you'll learn some new things about the ligands, the molecules that Ryan's talking about that can predict potential targets. So can you use that information of previous protein interactions or previous binding to known existing targets with some of the structures and so forth to build a model that will capture that essence of what we have learnt from the docking before? And so that's the second level of how do we infuse Artificial Intelligence. The third level, is to say okay, I can do this for a database of molecules, but then what if the protein-protein interactions are all over the literature study for millions of other viruses? How do I connect the dots across different mechanisms of actions too? Right and so this is where the knowledge graph component that Ryan was talking about comes in. So we've put together a database of about 150 billion medical facts from literature that Ryan is able to connect the dots and say okay, I'm starting with this molecule, what interactions do I know about the molecule? Is there a pretty intruding interaction that affects the mechanism of pathway for the symptoms that a disease is causing? And then he can go and figure out which protein and protein in the virus could potentially be working with this drug so that inhibiting certain activities would stop that progression of the disease from happening, right so like I said, your method of options, the options you've got is going to be, how much do you know about the target? How much do you know the drug database that you have and how much information can you leverage from previous research as you go down this pipeline, right so in that sense, I think we mix and match different methods and we've actually found that, you know mixing and matching different methods produces better synergies for people like Ryan. So. >> Well, the synergies I think is really important concept, Rangan, in additivities, synergistic, however you want to catch that. Right. But it goes back to your initial question Dr. Goh, which is this idea of polypharmacology and historically what we've done with traditional medicines there's more than one active, more than one network that's impacted, okay. You remember how I sort of put you on both ends of the spectrum which is the traditional sort of approach where we really don't know much about target ligand interaction to the completely interpretal side of it, right where now we are all, we're focused on is, in a single molecule interacting with a target. And so where I'm going with this is interesting enough, pharma has sort of migrate, started to migrate back toward the middle and what I mean by that, right, is we had these in a concept of polypharmacology, we had this idea, a regulatory pathway of so-called, fixed drug combinations. Okay, so now you start to see over the last 20 years pharmaceutical companies taking known, approved drugs and putting them in different combinations to impact different diseases. Okay. And so I think there's a really unique opportunity here for Artificial Intelligence or as Rangan has taught me, Augmented Intelligence, right to give you insight into how to combine those approved drugs to come up with unique indications. So is that patentability right, getting back to right how is it that it becomes commercially viable for entities like pharmaceutical companies but I think at the end of the day what's most interesting to me is sort of that, almost movement back toward that complex mixture of fixed drug combination as opposed to single drug entity, single target approach. I think that opens up some really neat avenues for us. As far as the expansion, the applicability of Artificial Intelligence is I'd like to talk to, briefly about one other aspect, right so what Rang and I have talked about is how do we take this concept of an active phytochemical and work backwards. In other words, let's say you identify a phytochemical from an in silico screening process, right, which was done for COVID-19 one of the first publications out of a group, Dr. Jeremy Smith's group at Oak Ridge National Lab, right, identified a natural product as one of the interesting actives, right and so it raises the question to our botanical guy, says, okay, where in nature do we find that phytochemical? What plants do I go after to try and source botanical drugs to achieve that particular end point right? And so, what Rangan's system allows us to do is to say, okay, let's take this phytochemical in this case, a phytochemical flavanone called eriodictyol and say, where else in nature is this found, right that's a trivial question for an Artificial Intelligence system. But for a guy like me left to my own devices without AI, I spend weeks combing the literature. >> Wow. So, this is brilliant I've learned something here today, right, If you find a chemical that actually, you know, affects and addresses a disease, right you can actually try and go the reverse way to figure out what botanicals can give you those chemicals as opposed to trying to synthesize them. >> Well, there's that and there's the other, I'm going to steal Rangan's thunder here, right he always teach me, Ryan, don't forget everything we talk about has properties, plants have properties, chemicals have properties, et cetera it's really understanding those properties and using those properties to make those connections, those edges, those sort of interfaces, right. And so, yes, we can take something like an eriodictyol right, that example I gave before and say, okay, now, based upon the properties of eriodictyol, tell me other phytochemicals, other flavonoid in this case, such as that phytochemical class of eriodictyols part right, now tell me how, what other phytochemicals match that profile, have the same properties. It might be more economically viable, right in other words, this particular phytochemical is found in a unique Himalayan plant that I've never been able to source, but can we find something similar or same thing growing in, you know a bush found all throughout the Southeast for example, like. >> Wow. So, Chris, on the pharmaceutical companies, right are they looking at this approach of getting, building drugs yeah, developing drugs? >> Yeah, absolutely Dr. Goh, really what Dr. Yates is talking about, right it doesn't help us if we find a plant and that plant lives on one mountain only on the North side in the Himalayas, we're never going to be able to create enough of a drug to manufacture and to provide to the masses, right assuming that the disease is widespread or affects a large enough portion of the population, right so understanding, you know, not only where is that botanical or that compound but understanding the chemical nature of the chemical interaction and the physics of it as well where which aspect affects the binding site, which aspect of the compound actually does the work, if you will and then being able to make that at scale, right. If you go to these pharmaceutical companies today, many of them look like breweries to be honest with you, it's large scale, it's large back everybody's clean room and it's, they're making the microbes do the work for them or they have these, you know, unique processes, right. So. >> So they're not brewing beer okay, but drugs instead. (Christopher laughs) >> Not quite, although there are pharmaceutical companies out there that have had a foray into the brewery business and vice versa, so. >> We should, we should visit one of those, yeah (chuckles) Right, so what's next, right? So you've described to us the process and how you develop your relationship with Dr. Yates Ryan over the years right, five years, was it? And culminating in today's, the many to many fast screening methods, yeah what would you think would be the next exciting things you would do other than letting me peek at your aha moments, right what would you say are the next exciting steps you're hoping to take? >> Thinking long term, again this is where Ryan and I are working on this long-term project about, we don't know enough about botanicals as much as we know about the synthetic molecules, right and so this is a story that's inspired from Simon Sinek's "Infinite Game" book, trying to figure it out if human population has to survive for a long time which we've done so far with natural products we are going to need natural products, right. So what can we do to help organizations like NCNPR to stage genomes of natural products to stage and understand the evolution as we go to understand the evolution to map the drugs and so forth. So the vision is huge, right so it's not something that we want to do on a one off project and go away but in the process, just like you are learning today, Dr. Goh I'm going to be learning quite a bit, having fun with life. So, Ryan what do you think? >> Ryan, we're learning from you. >> So my paternal grandfather lived to be 104 years of age. I've got a few years to get there, but back to "The Infinite Game" concept that Rang had mentioned he and I discussed that quite frequently, I'd like to throw out a vision for you that's well beyond that sort of time horizon that we have as humans, right and that's this right, is our current strategy and it's understandable is really treatment centric. In other words, we have a disease we develop a treatment for that disease. But we all recognize, whether you're a healthcare practitioner, whether you're a scientist, whether you're a business person, right or whatever occupation you realize that prevention, right the old ounce, prevention worth a pound of cure, right is how can we use something like Artificial Intelligence to develop preventive sorts of strategies that we are able to predict with time, right that's why we don't have preventive treatment approach right, we can't do a traditional clinical trial and say, did we prevent type two diabetes in an 18 year old? Well, we can't do that on a timescale that is reasonable, okay. And then the other part of that is why focus on botanicals? Is because, for the most part and there are exceptions I want to be very clear, I don't want to paint the picture that botanicals are all safe, you should just take botanicals dietary supplements and you'll be safe, right there are exceptions, but for the most part botanicals, natural products are in fact safe and have undergone testing, human testing for thousands of years, right. So how do we connect those dots? A preventive strategy with existing extent botanicals to really develop a healthcare system that becomes preventive centric as opposed to treatment centric. If I could wave a magic wand, that's the vision that I would figure out how we could achieve, right and I do think with guys like Rangan and Chris and folks like yourself, Eng Lim, that that's possible. Maybe it's in my lifetime I got 50 years to go to get to my grandfather's age, but you never know, right? >> You bring really, up two really good points there Ryan, it's really a systems approach, right understanding that things aren't just linear, right? And as you go through it, there's no impact to anything else, right taking that systems approach to understand every aspect of how things are being impacted. And then number two was really kind of the downstream, really we've been discussing the drug discovery process a lot and kind of the kind of preclinical in vitro studies and in vivo models, but once you get to the clinical trial there are many drugs that just fail, just fail miserably and the botanicals, right known to be safe, right, in many instances you can have a much higher success rate and that would be really interesting to see, you know, more of at least growing in the market. >> Well, these are very visionary statements from each of you, especially Dr. Yates, right, prevention better than cure, right, being proactive better than being reactive. Reactive is important, but we also need to focus on being proactive. Yes. Well, thank you very much, right this has been a brilliant panel with brilliant panelists, Dr. Ryan Yates, Dr. Rangan Sukumar and Chris Davidson. Thank you very much for joining us on this panel and highly illuminating conversation. Yeah. All for the future of drug discovery, that includes botanicals. Thank you very much. >> Thank you. >> Thank you.

>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards. Brought to you by Amazon Web Services. >> Hi friend, welcome to this CUBE coverage of AWS Public Sector Partner Awards Program. I'm John Furrier your host of theCUBE. We've two great guests here, Travis Hartman Director of Analytics and Weather at Maxar Technologies, and Vijay Tallapragada who's the Chief Modeling and Data Assimulation Branch at NOAH. Tell us about the success of this. What's the big deal? Take us through the award and why Maxar. What do you guys do? >> Yeah, so Maxar is an organization that does a lot of different activities in earth intelligence as well as space. We have about 4,000 employees around the world. One side of the economy works on space infrastructure actually building satellites, and all the infrastructure that's going to help get us back to the moon, and things like that, and then on the other side we have an earth intelligence group which is where I sit, and we leverage remote sensing information, earth science information to help people better understand how and what they do might impact the earth, or how the earth, in its activities, might impact their business mission or operations. So what we wanted to set out to do is help people better understand how weather could impact their mission, businesses, or operations. A big element of that was doing it with speed. So we knew NOAH had capabilities of running numerical weather prediction models and very traditional on-prem, big, beefy, high performance supercomputers, but we wanted to do it in the cloud. We wanted to use AWS as a key partner. So we collaborated with Vijay and NOAH and his teams there to help pull that off. They gave us access, public domain information but they showed us the right places to look. We've had some of our research scientists talkin' and yeah, after a pretty short effort, it didn't take a lot of time, we were able to pull something off a lot of people didn't think was possible. And we got pretty excited once we saw some of the outcomes. >> Travis, Vijay was just mentioning the relationship. Can you talk about the relationship together? Because this is not your classic Amazon Partner client relationship that you have. You guys have been partnering together, Vijay and your team, with AWS. Talk about the relationship and how Amazon played because it's a unique partnership. Explain in more detail, that specific relationship. >> Yeah, with Maxar and AWS, our partnership has gone back a number of years. Maxar being a fairly large organization, there's lots of different activities. I think Maxar was the first client of AWS Snowmobile where they had the big tractor trailer backed up to a data center, load all the data in, and then take it to an AWS data center. We were the first users of that 'cause we had over a hundred petabytes of satellite imagery in an archive that just movin' it across the internet it'd probably still be goin'. So the Snowmobile was a good success story for us but just with the amount of data that we have, the amount of data we collect every day, and all the analytics that we're running on it, whether it's in an HPC environment or the scalable AIML, we're able to scale out that architecture, scale out the compute, the much easier dynamic and really cost-effective way with AWS 'cause when we don't need to use the machines, we turn 'em off. We don't have a big data center sittin' somewhere where we have to have security, have all the overhead costs of just keeping the lights on, literally. AWS allows us to run our organization in a much more efficient way. And NOAH, they're seeing some of that same success story that we're seeing, as far as how they could use the cloud for accelerating research, accelerating how the advancement of numerical weather prediction from the United States can benefit from cloud, from cloud architecture, cloud compute, and things like that. And I think a lot of the stuff that we've done here at Maxar, with our HPC solution in the cloud is something that's pretty interesting to NOAH and it's a good opportunity for us to continue our collaboration. >> If I could drill down on that solution architecture for a minute, how did you guys set up the services and what lessons did you learn from that process? >> We're still learnin' is probably the short answer, but it all started with our people. We have some really strong engineers, really strong data scientists that fundamentally have a background in meteorology or atmospheric science, so they understand the physics of, you know, why the wind blows the way it does and why clouds do what clouds do. But we also, having a key strategic partnership with AWS, we were able to tap into some of their subject-matter experts, and we really put those people together and come up with new solutions and new, innovative ideas, stuff that people hadn't tried before. We were able to steer a little bit of AWS's product roadmap as far as what we were tryin' to do and how their current technology might not have been able to support it, but by interacting with us, gave them some ideas as far as what the tech had to move towards, and then that's what allowed us to move in a pretty quick fashion. It's neat stuff, technology, but it really comes down to the people. I feel very honored and privileged to work with both great people here, at Maxar, as well as AWS, as well as bein' able to collaborate with the great teams at NOAH. It's been a lot of fun. >> Well Travis, got a great example, I think it's a template that can be applied to many other areas, certainly even beyond. You've got a large scale, multi-scale situation, there. Congratulations. Final question, what does it mean to be an award winner for AWS Partner Awards? As part of the show, you're the best-in-show for HPC. What's it like? What's the feeling? Give is a quick stub from the field. >> Yeah, I mean, I don't know if there's really a lot of good words that can kind of sum it up. I shared the news with the team last night and you know, there were a lot of, lot of good responses that came from it. A lot of people think it's cool, and at the end of the day, a lot of people on our team took a hobby or a passion of weather and turned it into a career. And being acknowledged and recognized by groups like AWS for best solution in a particular thing, I think we take a lot of that to heart and we're very honored and proud of what we're able to do and proud that other people recognize the neat stuff that we're doin'. >> Well, certainly takin' advantage of the cloud which is large scale, but you're on a great wave, you've got a great area. I mean, weather, you talk about weather, it's exciting, dynamic, it's always changing, it's big data, it's large scale. So you got a lot of problems to solve and a lot of impact too, when you get it right. So congratulations on an excellent-- >> Thank you very much. >> Great mission. >> Thank you. >> Love what you do, love to followup again and maybe do another interview, and talk about the impact of weather and all the HPC kind of down the road. Travis, thank you very much. >> Thank you, appreciate it. >> Good to see you. >> Thank you, glad to be here. >> So NOAH, National Oceanic Atmospheric Administration, National Weather Center, National Center for Environmental Predictions, Environmental Modeling Center, that's your organization. You guys are competing to be the best in the world. Tell us what you guys do at a high level, then we'll jump into some of the successes. >> So the National Weather Service is responsible for providing weather forecasts to save lives and property, and improve the economy of the nation. And as part of that, the National Weather Service is responsible for providing data and also the forecast to the public and to the industry. We are responsible for providing the guidance on how they create the forecasts. So we are, at the Environmental Modeling Center, the nation's finest institute in advancing our numerical weather prediction modeling, government, and a nucleation of all the data that's available from the world to initialize our models and provide the future state of the atmosphere from hours all the way to seasons and years. And that's the kind of the range of products that we download and provide. Our key for managing the emergency of services and hazard management and mitigation, and also improve in the nation's economy by preparing well in advance, for the future events. And it's a science-based organization and we have world-class scientists working in this organization. I manage about 170 of them at the Environmental Modeling Center. They're all PhDs from various disciplines, mostly from meteorology, atmospheric sciences, oceanography, land surface modeling, space weather, all weather-related areas, and the mathematics and computer science. And we are at the stage where we are probably the most doubled up, advanced modeling center that we use almost all possible computational services available in the world, so this is heavily computational in terms of use of data, use of computers, use of all the power that we can get, and we have a 3.5 protoflop machine that we use to provide these weather forecasts. And they provide these services every hour for some census like we see the weather outbreaks and for every three hours for hurricanes, and for every six hours for the regular weather like precipitation, the temperature forecasts. So all the data that you see coming out from either the public media or the government agencies, they all are originated in our center and disseminated in various forms. And I think NOAH is the only center in the world that provides all this information free of cost. So it is a public service organization and we pride in our service to the society. >> Well, I love your title, Chief Modeling and Data Assimulation title, branch over all these organizations. This is, weather's critical. I want to get your thoughts 'cause we were talking before you came on about how the hurricane Katrina was something that really kind of forced everyone to kind of rethink things. Weather is an evolving system so it's always changing. Either there's a catastrophe or something happens, or you're trying to be proactive, predicting say, whether it's a fire season in California, all kinds of things goin' on. It's always hard to get a certain prediction. You have big jobs, there's a lot of data, you need horsepower, you need computing, you need to stand up some HPC. Take us through the thinking around the organization and what's the impact that you see, because weather does have that impact. >> So traditionally, you know, as you mentioned there are various weather phenomena that you described like the fiber of the hurricanes, the heavy precipitation, the flooding, so we download solutions for individual weather phenomena. And we have grown in that direction by downloading separate solutions for separate problems. And very soon, it became obvious that we cannot manage all these independent modeling systems to provide the best possible forecasts. So the thinking had to be changed. And then there is another bigger problem is that there's a lot of research going out in the community, like the academic institutes, the universities, other government labs. There are several people working in these areas and all their work is not necessarily a coordinated government act duty, that we cannot take advantage, and there are no incentives for people to come and contribute towards the mission that we are engaged in. So that actually prompted to change the direction of thinking, and as you mentioned, hurricane Katrina was an eye-opener. We have the best forecasts, but the dissemination of that information was not probably accurate enough, and also there is a lot of room for improvement in predicting these catastrophic events. >> How are you guys using AWS? Because HPC, high performance computing, I mean, you can't ask for more resources than the massive cloud that is Amazon. How has that helped you? Can you take a minute to explain, walk us through AWS partnership? >> There are a few examples I can cite, but before then, I would really like to appreciate Travis Hartman from Maxar who is probably the only private sector partner that we had in the beginning. And now, we are expanding on that. So we were able to share our immunity cords with Maxar and with our help, they were able to establish this entire modeling system as it is done in operations at NOAH. They were able to reproduce our operational forecasts using the cloud resources and then they went ahead and did even more by scaling the modeling systems as they can run even faster and quicker than what NOAH operations can do. So that gives you one example of how the cloud can be used. You know, the same forecast that we produce globally, which will take about eight minutes per day, and Maxar was able to do it much faster, like 50% improvement in the efficiency of the cords. And now, the one advantage of this is that the improvements that Maxar or other collaborators are using our cords, that they're putting into the system, are coming back to us. So we take advantage of that in improving the efficiency in operations. So this like a win-win situation for both of what part is fitting in the R&D and what using in operations. And on top of it, you can create multiple conflagrations of this model in various instances on the cloud where you can run it more efficiently and you can create an ensemble of solutions that can be catered to individual needs. And the one additional thing I wanted to mention about the user cloud is that this is like when you have a need, you can surge the compute, you can instantiate thousands of simulations to test a new innovation, for instance. You don't need to wait for the resources to be done in sequential manner. Instead, you can ramp up the production of these equipments in no time, and without worrying about, of course, the cost is a factor that we need to worry about, but otherwise the capacity is there, the facilities are there to take advantage of the cloud solutions. >> Well Vijay, I'm very impressed with your organization. I'd love to do a followup with you. I love the impact that you're doing. Certainly, the weather impacts society from forecasting disasters and giving people the ability to look at supply chain, whether it's planning for potentially a fire season or a water shortage, or anything goin' on, there. But also it's a template. You are succeeding a new kind of way to innovate with community, with large scale, multi-scale data points, so congratulations. >> Thank you. >> Thank you very much. I'm John Furrier here, part of AWS Partner Awards Program, best HPC solution. Great example, great use case, great conversation. Thanks for watching. Two great interviews here, as part of AWS Public Sector Partner Awards Program. I'm John Furrier. The best-in-show for HPC solutions, Travis Hartman, Maxar Technologies, and Vijay Tallapragada at NOAH, two great guests. Thanks for watching. (soft electronic music)

>>from around the globe. It's >>the Cube with digital coverage of >>AWS Public Sector Partner Awards >>brought to you by >>Amazon Web services. Everyone, welcome to this cube coverage of AWS Public Sector Partner Awards program. I'm John Furrow, your host of the Cube with two great guests here. Travis Department director of analytics and Weather at Max. Our technologies and VJ teleplay Gotta Who's the chief? Modeling and data a simulation branch at Noah. Tell us about the success of this. What's the big deal? Take us through the award and why Max are what you guys do. >>Yeah, so Macs are is an organization. Does a lot of different activities unearth intelligence as well as space? We have about 4000 employees around the world. One side of the economy works on space infrastructure, actually building satellites on all the infrastructure that's going to help us get us back to the moon and things like that. And then on the other side we have a north of intelligence group, which is where, I said, and we leverage remote sensing information for science information to help people better understand how, how and what they do might impact the Earth or have the earth, and it's activities might impact their business mission. Our operation. So what we wanted to set out to do was help people better understand how weather could impact their mission, business or operations. And a big element of that was doing it with speed. Ah, so we we knew? No. I had capabilities running America weather prediction models and very traditional on Prem. Big, beefy ah, high performance compute supercomputers. But we wanted to do it in The cloud we want to do is AWS is a key part. So we collaborated with B. J and Noah and his team is there to help pull that off. They gave this access public domain information, but they showed us the right places to look. We've had some of the research scientists talking, and after pretty short effort, it didn't take a lot of time. We were able to pull something off that a lot of people didn't think was possible. I'm we got pretty excited. Once we saw some of the outcome >>Travis to be, Jay was just mentioning the relationship. Can you talk about the relationship together because this is not your classic Amazon partner client relationship that you have. You guys have been partnering together V. J and your team with AWS. Talk about the relationship and that and how Amazon plays because it's a unique partnership plane in more detail at specific relationship. >>Yeah, with Max or in AWS. You know, our partnership has gone back A number of years on Macs are being a fairly large organization. There's lots of different activities. I think Max Star was the first client of AWS Snowmobile, where they have the big tractor trailer back up to a data center, load all the data in and then take it to an AWS data center. We were the first users of that because we had over 100 petabytes of satellite imagery and archive that just moving across the Internet would probably still be going. Um, so the snowmobile is a good success story for us, but just with >>the >>amount of data that we have, the amount of data we collect every day and all the analytics that we're running on it, whether it's in an HPC environment or, you know, the scalable Ai ml were able to scale out that architecture scale out that compute the much easier, dynamic and really cost effective way with AWS, because when we don't need to use the machines, we turn them off. We don't have a big data center sitting somewhere. We have to have security, have all the overhead costs of just keeping the lights on. Literally. AWS allows us to run our organization and a much more efficient way. Um and Noah, you know, they're They're seeing some of that same success story that we're seeing as far as how they can use the cloud for accelerating research, accelerating how the advancement of numerical weather prediction from the United States can benefit from cloud from cloud architecture, cloud computer, things like that. And I think a lot of the stuff that we've done here, Max our with our HPC HPC solution in the cloud. It's something that's pretty interesting to know, and it's it's a good opportunity for us to continue our collaboration. >>If I could drill down on that solution architecture for a minute. How did you guys set up the services, and what lessons did you learn from that process? >>We're still learning. It was probably the the short answer, but it all started with our people. Uh, you know, we have some really strong engineers, really strong data scientists that fundamentally have a background in meteorology or atmospheric science, you know? So they understand the physics. So you know why the wind blows is the way it doesn't. Why Cloud's doing clouds to do, Um, but we also having a key strategic partnership with AWS. We really have to tap into some of their subject matter experts. And we really put those people together, you know, and come up with new solutions, new innovative ideas, stuff that people hadn't tried before. We're able to steer a little bit of AWS is product roadmap for is what we were trying to do and how their current technology might not have been able to support it. But by interacting with us gave them some ideas as far as what the tech had to move towards. And then that's that's what allowed us to move pretty quick fashion. Um, you know, it's it's neat stuff technology, but it really comes down to the people. Um, and I feel very honored and privileged to work with both great people here. Attacks are as well as aws, um, as well as being able to collaborate with your great teams. That power, it's been a lot of fun. Well, >>Travis gonna create example? I think it's a template that could be applied to many other areas, certainly even beyond. You've got large scale, multi scale situation there. Congratulations. Final question. What does it mean to be an award winner for AWS Partner Awards as part of the show? You're the best in show for HPC. What's it like? What's the feeling? Give us a quick side from the field? >>Yeah. I mean, I don't know if there's really a lot of good words that kind of sum it up. It's Ah, I shared the news with the team last night, and you know, there are a lot of a lot of good responses that came from a lot of people think it's cool. And at the end of the day, a lot of people on our team, you know, took a hobby or a passion of weather and turned it into a career. Ah, and being acknowledged and recognized by groups like AWS for best solution in a particular thing. Um, I think we take a lot of that to heart. And, ah, we're very honored and proud of what we were able to do and proud that other people recognize the need stuff that we're doing well, >>Certainly taking advantage. The cloud, which is large scale. But you you're on a great wave. You've got a great area. I mean, whether you talk about whether it's exciting, it's dynamic. It's always changing. It's big data. It's large scale. So you get a lot of problems to solve in a lot of impact to get it right. So congratulations on ECs. >>Thank you very much. Great mission. Thank you. >>Love what you do love to follow up again. Maybe do another interview and talk about the impact of weather and all the HPC kind of down the road. But, Travis, thank you very much. >>Thank you. Appreciate it. >>Good to see you. >>Thank you. Good to be here. >>So Noah, National Oceanic Atmospheric Administration, National Weather Center, National Center for Environmental Predictions, Environmental Modeling Center year. That's your organization? You guys are competing to be best in the world. Tell us what you guys do at a high level. Then we'll jump into some of the successes. >>So the national Weather Service is responsible for providing weather forecast to save lives and property and improve the economy of the nation. And that's part of that. That the national weather services responsible for providing data and also the forecasts to the public and the industry and be responsible for providing the guidance on how they create the forecasts. So we are at the Environmental Modeling Center, uh, the nation's finest institute in advancing our numerical weather prediction modelling development, and you play it off all the data that's available from the world to initialize our models and provide the future state of the atmosphere from hours all the way to seasons and years. That's that's the kind of a range of products that we don't lock and provide are our key for managing the emergency services and patch it management and mitigation and also improving the nation's economy by preparing well in advance for the future events. And it's it's a science based organization, and we have ah well class scientists working in this organization. I manage about 170 of them at the moment of modeling center. They're all PhDs from various disciplines, mostly from meteorology, atmospheric sciences, oceanography, land surface modelling space weather, all weather related areas and the mathematics and computer science. And we are at the stage where we are probably the most. Uh huh. Most developed, uh, advanced modelling center that we use almost all possible computational resources available in the world. So this is a really computational in terms of user data, user computer seems off. Uh, all the power that we can get and we have a 3.5 petaflop machine that we use to provide these weather forecasts, and they provide the services every hour. For some sense is like the CDO rather our rates for every three hours for hurricanes and for every six hours for the regular, Rather like the participation, uh, the temperature forecast. So all the data that you see coming out from either the public media, our department agencies, they are originated in our center and disseminated in various forms. I think no one is the only center in the world that provides all this information for your past. So it is, ah, public service organization and we riding on a visa with society. >>We'll I love your title, Chief modeling and data, a simulation title branch of a lot of these organizations. This >>is >>whether it's ever critical. I want to get your thoughts cause we were talking before we came on about how the Hurricane Katrina was something that really kind of forcing you to rethink things. Whether it is an evolving system, it's always changing. Either the catastrophe or something happens. Were you trying to proactive predicting, say, whether it's a fire season in California, all kinds of things going on that's not It's always hard to get a certain prediction. You have big job. It's a lot of data you need. Horsepower need computing. You need to stand up. Some HPC take us through like like the thinking around the organization. And what was The impact is that you see, because whether does have that impact. >>So traditionally, you know, as you mentioned, there are radius weather phenomenon that you describe like the five rather the Americans, every presentation, the flooding. So we developed solutions for individual weather phenomena, and, uh, we have grown in that direction by developing separate solutions for separate problems. And very soon it became obvious that we cannot manage all these independent modeling systems to provide the best possible forecasts. So the thinking has to be changed. And then there is Another big problem is that there's a lot of research going out in the community like the academic institutes, the universities, other government labs. There are several people working in these areas, and all their work is not necessarily a coordinated, uh, development activity that we cannot take advantage. And they have no incentive for people to come and contribute towards the mission that we are engaged in. So that actually prompted to change the direction of thinking. And as you mentioned, Hurricane Katrina was an eye opener. We had the best forecasts, but the dissemination of that information waas not probably accurate enough, and also there is a lot of room for improvement in predicting these catastrophic events. How are >>you guys using AWS? Because HPC high performance computing I mean you can't ask for more resources in the massive cloud that is Amazon. How is that help to you? Can you take a minute to explain, but walk us through? >>What? >>Aws? There >>are a few example. Second site. But before then, I would like to really appreciate a Travis Hartman from Max. Are you know who is probably the only private sector partner that we had in the beginning. And now we're expanding on. That s so we were able to share our community. Cores with Max are and without how they were able to establish this and drive modeling system as it is done in operations that Noah and they were able to reproduce operational forecast using the cloud resources. And then they went ahead and did even more by scaling the modeling systems is that it can run even faster and quicker them are what insert no operations can do. So that gives us one example of how the cloud can be used. You know, the same forecast that we produce, ah, globally, which will take about eight minutes per day. And, uh, Max I was able to do it much faster, like 50% improvement and in the efficiency of the colors. And now the one piece of this is that the improvements that matter are other collaborators are using, or cords that they're putting into the system are coming back to us. So we take advantage of that, improving the efficiency in operations. So this is that this is like a win win situation for both, uh, who are participating in the R and D on who are using it in operations, and on top of it, you can create multiple configurations of this model in various instances on the cloud when you can run it more efficiently and you can create an ensemble of solutions that can be captured toe individual needs. And the one additional thing I want to mention about User Cloud is, is that you know, this is like when you have a need, you can search the compute you can. Instead she 8000 sub simulations to test a new innovation. For instance, you don't need to wait for the resources to be done in a sequential manner. Instead, you can ramp up the production off these apartments in no kind and without Don't worry about. Of course, the cost is the fact that we need to worry about, but otherwise the capacity is there. The facilities are reacting to take advantage of the cloud solutions. If I'm a >>computer scientist person, I'm working on a project. Now I have all this goodness in the cloud, how's morale been and what's the reaction been like from from people doing the work. Because usually the bottleneck has been like I gotta provision resource. I gotta send a procurement request for some servers or I want to really push some load. And right now, I got a critical juncture. I mean, it's got a push morale up a bit, and you talk about the impact to the psychology of the people in your organization. >>Um, I haven't. I have two answers to this question. One from a scientist perspective like me. You know, I was not a computer scientist from the beginning, but I became a software engineer, kind of because I have to work with these software and hardware stuff more more on solving the computational problems than the critical problems. So people like us who have invested their careers in improving the science, they were not care whether it's ah, uh hbc on premise Cloud, what will be delighted to have, uh, resources available alleviate that they can drive. But on the other hand, the computer computational engineers are software engineers who are entering into this field. I think they are probably the most excited because of these emerging opportunities. And so there is a kind of a friction between the scientific and the computational aspects off personnel, I would say. But that difference is slowly raising on and we are working together as never before. So the collective moral is very high to take advantage of these resources and opportunities. I think way of making the we're going in the right direction. >>It's so much faster. I mean, in the old days, you write a paper, you got to get some traction. Gonna do a pilot now It's like you run an experiment, get it out there. VJ I'm very impressed with the organization. Love to do a follow up with you. I love the impact that you're doing certainly in the weather impact society from forecasting disasters and giving people the ability to look at supply chain, whether it's providing for potentially a fire season or water shortage or anything going on there. But also it's a template. You're exceeding a new kind of waiting to innovate with community with large scale, multi scale data points. So congratulations and >>thank you. >>Thank you very much. I'm John Furrier here part of AWS partner Awards program. Best HPC solution. Great. Great Example. Great use case. Great conversation. Thanks for watching two great interviews. Here is part of AWS Public Sector Partner Awards program. I'm John Furrier. The best in show for HPC Solutions. China's Hartman Max, our technologies and Vijay tell Apartado at Noah. Two great guests. Thanks for watching. Yeah, Yeah, yeah, yeah, yeah, yeah

>> Narrator: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2017, presented by AWS, Intel, and our ecosystem of partners. >> Hello everyone, welcome to a special CUBE presentation here, live in Las Vegas for Amazon Web Service's AWS re:Invent 2017. This is theCUBE's fifth year here. We've been watching the progression. I'm John Furrier with Justin here as my co-host. Our two next guests are Bob Rogers, the chief data scientist at Intel, and Julie Cardoa, who's the CEO of Thorn. Great guests, showing some AI for good. Intel, obviously, good citizen and great technology partner. Welcome to theCUBE. >> Thank you, thanks for having us! >> So, I saw your talk you gave at the Public Sector Breakfast this morning here at re:Invent. Packed house, fire marshal was kicking people out. Really inspirational story. Intel, we've talked at South by Southwest. You guys are really doing a lot of AI for good. That's the theme here. You guys are doing incredible work. >> Julie: Thank you. >> Tell your story real quick. >> Yeah, so Thorn is a nonprofit, we started about five years ago, and we are just specifically dedicated to build new technologies to defend children form sexual abuse. We were seeing that, as, you know, new technologies emerge, there's new innovation out there, how child sexual abuse was presenting itself was changing dramatically. So, everything from child sex trafficking online, to the spread of child sexual abuse material, livestreaming abuse, and there wasn't a concentrated effort to put the best and brightest minds and technology together to be a part of the solution, and so that's what we do. We build products to stop child abuse. >> John: So you're a nonprofit? >> Julie: Yep! >> And you're in that public sector, but you guys have made a great progress. What's the story behind it? How did you get to do so effective work in such a short period of time as a nonprofit? >> Well, I think there's a couple things to that. One is, well, we learned a lot really quickly, so what we're doing today is not what we thought we would do five years ago. We thought we were gonna talk to big companies, and push them to do more, and then we realized that we actually needed to be a hub. We needed to build our own engineering teams, we needed to build product, and then bring in these companies to help us, and to add to that, but there had to be some there there, and so we actually have evolved. We're a nonprofit, but we are a product company. We have two products used in 23 countries around the world, stopping abuse every day. And I think the other thing we learned is that we really have to break down silos. So, we didn't, in a lot of our development, we didn't go the normal route of saying, okay, well this is a law enforcement job, so we're gonna go bid for a big government RFE. We just went and built a tool and gave it to a bunch of police officers and they said, "Wow, this works really well, "we're gonna keep using it." And it kinda spread like wildfire. >> And it's making a difference. It's really been a great inspirational story. Check out Thorn, amazing work, real use case, in my mind, a testimonial for how fast you can accelerate. Congratulations. Bob, I wanna get your take on this because it's a data problem that, actually, the technology's applying to a problem that people have been trying to crack the code on for a long time. >> Yeah, well, it's interesting, 'cause the context is that we're really in this era of AI explosion, and AI is really computer systems that can do things that only humans could do 10 years ago. That's kind of my basic way of thinking about it, so the problem of being able to recognize when you're looking at two images of the same child, which is the piece that we solved for Thorn, actually, you know, is a great example of using the current AI capabilities. You start with the problem of, if I show an algorithm two different images of the same child, can it recognize that they're the same? And you basically customize your training to create a very specific capability. Not a basic image recognition or facial recognition, but a very specific capability that's been trained with specific examples. I was gonna say something about what Julie was describing about their model. Their model to create that there there has been incredible because it allows them to really focus our energy into the right problems. We have lots of technology, we have lots of different ways of doing AI and machine learning, but when we get a focus on this is the data, this is the exact problem we need to solve, and this is the way it needs to work for law enforcement, for National Center for Missing and Exploited Children. It has really just turned the knob up to 11, so to speak. >> I mean, this is an example where, I mean, we always talk about how tech transformation can make things go faster. It's such an obvious problem. I mean, it's almost everyone kinda looks away because it's too hard. So, I wanna ask you, how do people make this happen for other areas for good? So, for instance, you know, what was the bottlenecks before? What solved the problem, because, I mean, you could really make a difference here. You guys are. >> Well, I think there's a couple things. I think you hit on one, which is this is a problem people turn away from. It's really hard to look at. And the other thing is is there's not a lot of money to be made in using advanced technology to find missing and exploited children, right? So, it did require the development of a nonprofit that said, "We're gonna do this, "and we're gonna fundraise to get it done." But it also required us to look at it from a technology angle, right? I think a lot of times people look at social issues from the impact angle, which we do, but we said, "What if we looked at it "from a different perspective? "How can technology disrupt in this area?" And then we made that the core of what we do, and we partnered with all the other amazing organizations that are doing the other work. And I think, then, what Bob said was that we created a hub where other experts could plug into, and I think, in any other issue area that you're working on, you can't just talk about it and convene people. You actually have to build, and when you build, you create a platform that others can add to, and I think that is one of the core reasons why we have seen so much progress, is we started out convening and really realized that wasn't gonna last very long, and then we built, and once we started building, we scaled. >> So, you got in the market quickly with something. >> Yeah. >> So, one of the issues with any sort of criminal enterprise is it tends to end up in a bit of an arms race, so you've built this great technology but then you've gotta keep one step ahead of the bad guys. So, how are you actually doing that? How are you continuing to invest in this and develop it to make sure that you're always one step ahead? >> So, I can address that on a couple of levels. One is, you know, working with Thorn, and I lead a program at Intel called the Safer Children Program, where we work with Thorn and also the National Center for Missing and Exploited Children. Those conversations bring in all of the tech giants, and there's a little bit of sibling rivalry. We're all trying to throw in our best tech. So, I think we all wanna do as well as we can for these partnerships. The other thing is, just in very tactical terms, working with Thorn, we've actually, Thorn and with Microsoft, we've created a capability to crowdsource more data to help improve the accuracy of these deep learning algorithms. So, by getting critical mass around this problem, we've actually now created enough visibility that we're getting more and more data. And as you said earlier, it's a data problem, so if you have enough data, you can actually create the models with the accuracy and the capability that you need. So, it starts to feed on itself. >> Julie talked about the business logic, how she attacked that. That's really, 'cause I think one thing notable, good use case, but from a tech perspective, how does the cloud fit in with Intel specifically? Because it really, the cloud is an enabler too. >> Bob: Yeah, absolutely. >> How's that all working with Intel? And you go on about whole new territory you guys are forging in here, it's awesome, but the cloud. >> Right, so, for us, the cloud is an incredible way for us to make our compute capability available to anyone who needs to do computing, especially in this data-driven algorithm era where more and more machine learning, more and more AI, more and more data-driven problems are coming to the fore, doing that work on the cloud and being able to scale your work according to how much data is coming in at any time, it makes the cloud a really natural place for us. And of course, Intel's hardware is a core component of pretty much all the cloud that you could connect to. >> And the compute that you guys provide, and Amazon adds to it, their cloud is impressive. Now, I'd like to know what you guys are gonna be talking about in your session. You have a session here at re:Invent. What's the title of the session, what's the agenda, is it the same stuff here, what's gonna be talked about? >> So, we're talking about life-changing AI applications, and in specific we're gonna talk about, at the end Julie will talk about what Thorn has done with the child-finder and the AI that we and Microsoft built for them. We'll also, I'll start out by talking about Intel's role broadly in the computing and AI space. Intel really looks to take all of its different hardware, and networking, and memory assets, and make it possible for anybody to do the kinds of artificial intelligence or machine learning they need to do. And then in the middle, there's a really cool deployment on AWS sandwich that (something) will talk about how they've taken the models and really dialed them up in terms of how fast you can go through this data, so that we can go through millions and millions of images in our searches, and come back with results really, really fast. So, it's a great sort of three piece story about the conception of AI, the deployment at scale and with high performance, and then how Thorn is really taking that and creating a human impact around it. >> So, Bob, I asked you the Intel question because no one calls up Intel and says, "Hey, give me some AI for good." I mean, I wish that would be the case. >> Well, they do now. >> If they do, well, share your strategy, because cloud makes sense. I could see how you could provision easily, get in there, really empowering people to do stuff that's passionable and relevant. But how do you guys play in all of this? 'Cause I know you supply stuff to the cloud guys. Is this a formal program you're doing at Intel? Is this a one-off? >> Yeah, so Safer Children is a formal program. It started with two other folks, Lisa Davis and Lisa Theinai, going to the VP of the entire data center group and saying, "There is an opportunity to make a big impact "with Intel technology, and we'd like to do this." And it started literally because Intel does actually want to do good work for humankind, and frankly, the fact that these people are using our technology and other technology to hurt children, it steams our dumplings, frankly. So, it started with that. >> You've been a team player with Amazon and everyone else. >> Exactly, so then, once we've been able to show that we can actually create technology and provide infrastructure to solve these problems, it starts to become a self-fulfilling prophecy where people are saying, "Hey, we've got this "interesting adjacent problem that "this kind of technology could solve. "Is there an opportunity to work together and solve that?" And that fits into our bigger, you know, people ask me all the time, "Why does Intel have a chief data scientist?" We're a hardware company, right? The answer is-- >> That processes a lot of data! >> Yes, that processes a lot of data. Literally, we need to help people know how to get value from their data. So, if people are successful with their analytics and their AI, guess what, they're gonna invest in their infrastructure, and it sort of lifts Intel's boat across the board. >> You guys have always been a great citizen, and great technology provider, and hats off to Intel. Julie, tell a story about an example people can get a feel for some of the impact, because I saw you on stage this morning with Theresa Carlson, and we've been tracking her efforts in the public sector have been amazing, and Intel's been part of that too, congratulations. But you were kind of emotional, and you got a lot of applause. What's some of the impact? Tell a story of how important this really is, and your work at Thorn. >> Yeah, well, I mean, one of the areas we work in is trying to identify children who are being sold online in the US. A lot of people, first of all, think that's happening somewhere else. No, that's here in this country. A lot of these kids are coming out of foster care, or are runaways, and they get convinced by a pimp or a trafficker to be sold into prostitution, basically. So, we have 150,000 escort ads posted every single day in this country, and somewhere in there are children, and it's really difficult to look through that with your eye, and determine what's a child. So, we built a tool called Spotlight that basically reads and analyzes every ad as it comes in, and we layer on smart algorithms to say to an officer, "Hey, this is an ad you need to pay attention to. "It looks like this could be a child." And we've had over 6,000 children identified over the last year. >> John: That's amazing. >> You know, it happens in a situation where, you know, you have online it says, you know, this girl's 18, and it's actually a 15-year-old girl who met a man who said he was 17, he was actually 30, had already been convicted of sex trafficking, and within 48 hours of meeting this girl, he had her up online for sale. So, that sounds like a unique incident. It is not unique, it happens every single day in almost every city and town across this country. And the work we're doing is to find those kids faster, and stop that trauma. >> Well, I just wanna say congratulations. That's great work. We had a CUBE alumni, founder of CloudAir, Jeff Hammerbacher, good friend of theCUBE. He had a famous quote that he said on theCUBE, then said on the Charlie Rose Show, "The best minds of our generations "are thinking about how to make people click ads. "That sucks." This is an example where you can really put the best minds on some of the real important things. >> Yeah, we love Jeff. I read that quote all the time. >> It's really a most important quote. Well, thanks so much. Congratulations, great inspiration, great story. Bob, thanks for coming on, appreciate it. CUBE live coverage here at AWS re:Invent 2017, kicking off day one of three days of wall-to-wall coverage here, live in Las Vegas. We'll be right back with more after this short break.

(electronic music) >> Announcer: Live from Austin, Texas, it's The Cube. Covering South By Southwest 2017. Brought to you by Intel. Now, here's John Furrier. >> Hey, welcome back, everyone, we're here live in Austin, Texas, for South By Southwest Cube coverage at the Intel AI Lounge, #IntelAI if you're watching, put it out on Twitter. I'm John Furrier of Silicon Angle for the Cube. Our next guest is Alison Yu who's with Cloudera. And in the news today, although they won't comment on it. It's great to see you, social media manager at Cloudera. >> Yes, it's nice to see you as well. >> Great to see you. So, Cloudera has a strategic relationship with Intel. You guys have a strategic investment, Intel, and you guys partner up, so it's well-known in the industry. But what's going on here is interesting, AI for social good is our theme. >> Alison: Yes. >> Cloudera has always been a pay-it-forward company. And I've known the founders, Mike Olson and Amr Awadallah. >> Really all about the community and paying it forward. So Alison, talk about what you guys are working on. Because you're involved in a panel, but also Cloudera Cares. And you guys have teamed up with Thorn, doing some interesting things. >> Alison: Yeah (laughing). >> Take it away! >> Sure, thanks. Thanks for the great intro. So I'll give you a little bit of a brief introduction to Cloudera Cares. Cloudera Cares was founded roughly about three years ago. It was really an employee-driven and -led effort. I kind of stepped into the role and ended up being a little bit more of the leader just by the way it worked out. So we've really gone from, going from, you know, we're just doing soup kitchens and everything else, to strategic partnerships, donating software, professional service hours, things along those lines. >> Which has been very exciting to see our nonprofit partnerships grow in that way. So it really went from almost grass-root efforts to an organized organization now. And we start stepping up our strategic partnerships about a year and a half ago. We started with DataKind, is our initial one. About two years ago, we initiated that. Then we a year ago, about in September, we finalized our donation of an enterprise data hub to Thorn, which if you're not aware of they're all about using technology and innovation to stop child-trafficking. So last year, around September or so, we announced the partnership and we donated professional service hours. And then in October, we went with them to Grace Hopper, which is obviously the largest Women in Tech Conference in North America. And we hosted a hackathon and we helped mentor women entering into the tech workforce, and trying to come up with some really cool innovative solutions for them to track and see what's going on with the dark web, so we had quite a few interesting ideas coming out of that. >> Okay, awesome. We had Frederico Gomez Suarez on, who was the technical advisor. >> Alison: Yeah. >> A Microsoft employee, but he's volunteering at Thorn, and this is interesting because this is not just donating to the soup kitchens and what not. >> Alison: Yeah. >> You're starting to see a community approach to philanthropy that's coding RENN. >> Yeah. >> Hackathons turning into community galvanizing communities, and actually taking it to the next level. >> Yeah. So, I think one of the things we realize is tech, while it's so great, we have actually introduced a lot of new problems. So, I don't know if everyone's aware, but in the '80s and '90s, child exploitation had almost completely died. They had almost resolved the issue. With the introduction of technology and the Internet, it opened up a lot more ways for people to go ahead and exploit children, arrange things, in the dark web. So we're trying to figure out a way to use technology to combat a problem that technology kind of created as well, but not only solving it, but rescuing people. >> It's a classic security problem, the surface area has increased for this kind of thing. But big data, which is where you guys were founded on in the cloud era that we live in. >> Alison: Yeah. >> Pun intended. (laughing) Using the machine learning now you start with some scale now involved. >> Yes, exactly, and that's what we're really hoping, so we're partnering with Intel in the National Center of Missing Exploited Children. We're actually kicking off a virtual hackathon tomorrow, and our hope is we can figure out some different innovative ways that AI can be applied to scraping data and finding children. A lot of times we'll see there's not a lot of clues, but for example, if we can upload, if there can be a tool that can upload three or four different angles of a child's face when they go missing, maybe what happens is someone posts a picture on Instagram or Twitter that has a geo tag and this kid is in the background. That would be an amazing way of using AI and machine learning-- >> Yeah. >> Alison: To find a child, right. >> Well, I'll give you guy a plug for Cloudera. And I'll reference Dr. Naveen Rao, who's the GM of Intel's AI group, was on earlier. And he was talking about how there's a lot of storage available, not a lot of compute. Now, Cloudera, you guys have really pioneered the data lake, data hub concept where storage is critical. >> Yeah. >> Now, you got this compute power and machine learning, that's kind of where it comes together. Did I get that right? >> Yeah, and I think it's great that with the partnership with Intel we're able to integrate our technology directly into the hardware, which makes it so much more efficient. You're able to compute massive amounts of data in a very short amount of time, and really come up with real results. And with this partnership, specifically with Thorn and NCMEC, we're seeing that it's real impact for thousands of people last year, I think. In the 2016 impact report, Thorn said they identified over 6,000 trafficking victims, of which over 2,000 were children. Right, so that tool that they use is actually built on Cloudera. So, it's great seeing our technology put into place. >> Yeah, that's awesome. I was talking to an Intel person the other day, they have 72 cores now on a processor, on the high-end Xeons. Let's get down to some other things that you're working on. What are you doing here at the show? Do you have things that you're doing? You have a panel? >> Yeah, so at the show, at South by Southwest, we're kicking off a virtual hackathon tomorrow at our Austin offices for South by Southwest. Everyone's welcome to come. I just did the liquor order, so yes, everyone please come. (laughing) >> You just came from Austin's office, you're just coming there. >> Yeah, exactly. So we've-- >> Unlimited Red Bull, pizza, food. (laughing) >> Well, we'll be doing lots and lots tomorrow, but we're kicking that off, we have representatives from Thorn, NCMEC, Google, Intel, all on site to answer questions. That's kind of our kickoff of this month-long virtual hackathon. You don't need to be in Austin to participate, but that is one of the things that we are kicking off. >> And then on Sunday, actually here at the Intel AI Lounge we're doing a panel on AI for Good, and using artificial intelligence to solve problems. >> And we'll be broadcasting that live here on The Cube. So, folks, SiliconAngle.tv will carry that. Alison, talk about the trend that, you weren't here when we were talking about how there's now a new counterculture developing in a good way around community and social change. How real is the trend that you're starting to see these hackathons evolve from what used to be recruiting sessions to people just jamming together to meet each other. Now, you're starting to see the next level of formation where people are organizing collectively-- >> Yeah. >> To impact real issues. >> Yeah. >> Is this a real trend or where is that trend, can you speak to that? >> Sure, so from what I've seen from the hackathons what we've been seeing before was it's very company-specific. Only one company wanted to do it, and they would kind of silo themselves, right? Now, we're kind of seeing this coming together of companies that are generally competitors, but they see a great social cause and they decide that they want to band together, regardless of their differences in technology, product, et cetera, for a common good. And, so. >> Like a Thorn. >> For Thorn, you'll see a lot of competitors, so you'll see Facebook and Twitter or Google and Amazon, right? >> John: Yeah. >> And we'll see all these different competitors come together, lend their workforce to us, and have them code for one great project. >> So, you see it as a real trend. >> I do see it as a trend. I saw Thorn last year did a great one with Facebook and on-site with Facebook. This year as we started to introduce this hackathon, we decided that we wanted to do a hackathon series versus just a one-off hackathon. So we're seeing people being able to share code, contribute, work on top of other code, right, and it's very much a sharing community, so we're very excited for that. >> All right, so I got to ask you what's they culture like at Cloudera these days, as you guys prepare to go public? What's the vibe internally of the company, obviously Mike Olson, the founder, is still around, Amr's around. You guys have been growing really fast. Got your new space. What's the vibe like in Cloudera now? >> Honestly, the culture at Cloudera hasn't really changed. So, when I joined three years ago we were much smaller than we are now. But I think one thing that we're really excited about is everyone's still so collaborative, and everyone makes sure to help one another out. So, I think our common goal is really more along the lines of we're one team, and let's put out the best product we can. >> Awesome. So, what's South by Southwest mean to you this year? If you had to kind of zoom out and say, okay. What's the theme? We heard Robert Scoble earlier say it's a VR theme. We hear at Intel it's AI. So, there's a plethora of different touchpoints here. What do you see? >> Yeah, so I actually went to the opening keynote this morning, which was great. There was an introduction, and then I don't know if you realized, but Cory Booker was on as well, which is great. >> John: Yep. >> But I think a lot of what we had seen was they called out on stage that artificial intelligence is something that will be a trend for the next year. And I think that's very exciting that Intel really hit the nail on the head with the AI Lounge, right? >> Cory Booker, I'm a big fan. He's from my neighborhood, went to the same school I went to, that my family. So in Northern Valley, Old Tappan. Cory, if you're watching, retweet us, hashtag #IntelAI. So AI's there. >> AI is definitely there. >> No doubt, it's on stage. >> Yes, but I think we're also seeing a very large, just community around how can we make our community better versus let's try to go in these different silos, and just be hyper-aware of what's only in front of us, right? So, we're seeing a lot more from the community as well, just being interested in things that are not immediately in front of us, the wider, either nation, global, et cetera. So, I think that's very exciting people are stepping out of just their own little bubbles, right? And looking and having more compassion for other people, and figuring out how they can give back. >> And, of course, open source at the center of all the innovation as always. (laughing) >> I would like to think so, right? >> It is! I would testify. Machine learning is just a great example, how that's now going up into the cloud. We started to see that really being part of all the apps coming out, which is great because you guys are in the big data business. >> Alison: Yeah. >> Okay, Alison, thanks so much for taking the time. Real quick plug for your panel on Sunday here. >> Yeah. >> What are you going to talk about? >> So we're going to be talking a lot about AI for good. We're really going to be talking about the NCMEC, Thorn, Google, Intel, Cloudera partnership. How we've been able to do that, and a lot of what we're going to also concentrate on is how the everyday tech worker can really get involved and give back and contribute. I think there is generally a misconception of if there's not a program at my company, how do I give back? >> John: Yeah. >> And I think Cloudera's a shining example of how a few employees can really enact a lot of change. We went from grassroots, just a few employees, to a global program pretty quickly, so. >> And it's organically grown, which is the formula for success versus some sort of structured company program (laughing). >> Exactly, so we definitely gone from soup kitchen to strategic partnerships, and being able to donate our own time, our engineers' times, and obviously our software, so. >> Thanks for taking the time to come on our Cube. It's getting crowded in here. It's rocking the house, the house is rocking here at the Intel AI Lounge. If you're watching, check out the hashtag #IntelAI or South by Southwest. I'm John Furrie. I'll be back with more after this short break. (electronic music)

(upbeat pop music) >> Narrator: Live from Austin, Texas, it's theCUBE. Covering South by Southwest 2017. Brought to you by Intel. Now, here's John Furrier. >> Okay welcome back everyone. We are here live at South by Southwest at the Intel AI lounge. This is SiliconANGLE's theCUBE, talking to some great guests. The theme for this week is AI for Social Good. I'm John Furrier with SiliconANGLE, our next guest is Federico Gomez Suarez, technical advisor and volunteer at Thorn, doing some really amazing things with technology for the betterment of society. Specifically a use case. So Federico, welcome to theCUBE, welcome to the AI Lounge here at Intel. >> Thank you very much for having me. >> So talk about Thorn. First of all, you work for Microsoft, but you're a volunteer? >> Correct. >> Talk about what Thorn is, and what you guys do. It's really a great story. >> So Thorn is a non-profit which focuses on driving technological innovation to fight child sexual exploitation. And it does it two ways. One of them is by doing research to find the new trends and the new ways that this is happening. But also by using the latest technology to find ways that we can actually fight this problem. Thorn has something called an innovation lab, where we're always trying new technology, we're trying AI just to find new ways to fight the problem. >> So this is really a great use case of where technology is being used for the betterment of society and good, because what you're doing is taking really cutting edge big data, machine learning, AI techniques. And the rage right now is facial recognition. >> Oh Yes! >> So talk about where and how it works. And what's the results? And can you share some of the impact? >> Yeah! So as part of my volunteer work, one of the projects that I have been working, is called a child finder service. And the idea of this work is, if we have an image, particularly an image of a child who have been missing, can we use facial recognition to determine whether another image is the same child. And this is actually a pretty challenging problem because the child may have gone missing many years back and now we want to match against another picture where the child may show much growth. >> Depending on the duration, right? >> And you know, if you imagine the impact of actually having this technology, a person who is trying to look for a missing child, if they have to go through a lot of pictures, it's actually hard to determine whether two people are the same person or not. So we're helping in that case. We're helping so that you don't have to go through so many pictures. So that we can highlight the ones that the machine thinks is actually the same person. >> Take us through how it works, in just a use case, just as an illustration. >> Yeah, So when a child goes missing, the National Center for Missing Children, which we work with, they publish a poster and that poster has an image of a missing child. Now once you have that image, you may want to say well are there places where the picture of that child may be showing up. One place that there's usually pictures of children being exploited are online ads. So let's say that there's online ads and you want to say, well in any of these ads that they use for exploitation, could there be the same child in both of them. So that's actually a use case. And just using face recognition technology, we can try to make the problem easier, faster than it would be if you were trying to do it manually. >> And you're doing a demo here in the Intel AI Lounge. What's in the demo? What are you showing? >> So in the demo, I'm showing how difficult it really is to do face recognition by hand. And how by just having some assistance from a machine, you can go from having to look at hundreds of images and spending potentially hours, to doing it seconds. >> So how to do you involved? I mean, this is a volunteer organization, take us through your journey. How did you get involved? And talk about how you guys are getting more people involved, and how can someone get involved? >> Absolutely! So, you know as for Microsoft, there is the Hack for Good community, and they encourage us to go and donate our time, our skill to non-profits. Two years ago, I had this idea, and I did a hackathon. And after the hackathon, I got connected with Thorn. I learn about what they do, and that's how I pretty much got involved. I was really fortunate that Microsoft supported me to actually go spend time with a non-profit. And when I start working with Thorn, I realized, hey there's other tech companies also willing to help. So in this child finder service project, I work with Intel, I work with other companies all coming together to find ways to solve this problem using the cutting edge technology available. And you know, Thorn is always looking for volunteers, we're looking for what we call our Tech Defenders. If you go to our website, which is wearethorn.org/Sxsw, you'll find the link where you can actually volunteer your skills as a technical defender for Thorn. >> So talk about, that's very cool by the way. People should check out Thorn. Is there a website, Thorn? >> Yeah, it's wearethorn.org/sxsw. >> Okay, wearethorn.org/sxsw. For South by Southwest. So talk about the technology, because obviously Intel makes chips, makes stuff go faster, you got more compute, you've got more cores, you got now, cloud technology. And you've seen at Google Next, where they were showcasing their Xeon processor, that the AI trend now is becoming really, really, really big. I know Microsoft as your Amazon web services. They're all having these machine learning libraries, and the big trend is self-learning machines or deep-learning. So this is a tech trend. But now when you apply it to this, it really can work. So, what is some of the technology, and what are some of the data sets that you use, how does it work under the covers? >> Yeah so, we actually start with an open source technology for face recognition. And after we started with this technology, we realized that we had to make it better. So we had to build data sets ourselves. For the data sets we have images of the posters that are published from the National Center. We have also started asking people to donate images over time, of themselves. Because we need images of people when they were children, and when they're older. And that's how we've been building data sets. And then having the data set, we need to go and train them. And that's where we're using hardware, in particularly using GPUs to actually do training is really is key for us. The technology really under this is deep learning for us. We used an existing deep-learning models, and improving them with our particular scenario, cause there's special challenges in our case. Not only with the age, but also a lot of the images that we process. Sometimes there's heavy makeup, sometimes there's things like that. >> Or res, resolution right? Depending on the photo? Right? >> Yeah. And you now, low resolution images particularly they're a challenge, so we need to improve it, we need to keep training to actually get to the point where we feel we have a really robust system. >> I want to ask you a personal question. And this is something we were talking about on our intro segment, and something that I've been thinking a lot about. I haven't written about it yet, but I've been starting to tease it out on some of my thought leader interviews. Is that, in every major inflection point in the business of technology, there's always been a counter-culture movement. And it seems to be that, if you look at all the news, whether it's political or tech company news, and all this stuff happening around the world, there seems to be a social good culture developing. We're seeing a counter-culture where what was once valued, tech or public proprietary algorithms, is now changing to open source, community, societal benefits. There seems to be a lot of activity, and no one's kind of put their finger on it. And you're a great use case of that example. >> And I feel like, the Hack for Good community in Microsoft is growing, and there's people, peers of mine, working on all this kind of interesting projects helping non-profits. >> And that's called Hack for Good? >> Yes. >> What's it called? >> Hack for Good in Microsoft. >> So that's a Microsoft hackathon with employees who just say, hey let's pick something good to do and they apply their programming technical skills to... >> Yeah, and you know there's a lot of support, and we're encouraged to do it. And it's to me inspiring to work in a company that really encourage that, and you know what? I see the same when I look across the industry. I see people willing to spend their evenings, like I spend my evenings working on some of this, or weekends, but we're passionate about making a difference. And I know I'm not alone. I've met a lot of people, and I know there's a lot more out there. >> Is there a community people can check out? Is it on the website? Is there open source community? Is there a certain software groups that are playing more than others? >> Actually I don't know. I know in my space, I think a I think a great place to start is joining Thorn's Digital Defenders. But I would say if someone is passionate about a cause, it could be anything, and say I want to help, there's non-profits out there for that. And when I work with non-profits, they're so passionate about it, and sometimes they just need help in little things. And having so many tech communities go in and help them makes a huge difference. I would invite people to just go. If you're passionate about it, just go for it. Find a non-profit, they'll be happy to work with you. >> Federico, I want to ask you if you could share just some anecdotal impact that you guys have had. Can you share some successes, some advances? Just highlight some of the things. >> Yeah, so Thorn just published their yearly report and it was really encouraging. So, Thorn has a couple of different tools that they build. One of them is called Spotlight. Through the use of this tool last year, about 2,000 children who were victims of trafficking, were recovered from around 6,000 victims. And you know, each victim is a person. And the fact that we're making a difference in those lives is extremely encouraging. And that's just one of the things that we were able to contribute. So that's one of the stories that we have. And to me it's not only that. To me, it's also the fact that I see people who are willing to actually get engaged, learn more about these problems is another huge win. >> Final question for you Federico. Describe the scene here at the AI Lounge at Intel. For folks watching who aren't at South by Southwest, what is the vibe here? What are they showing? Obviously AI is the theme. AI for Social Good is our broadcast here. Hashtag is #intelai, if you're interested in sharing, we'd appreciate if you could retweet and share the love. What's your thoughts on with the vibe here? Describe the scene here. >> You know, when I look around, all the demos are amazing. Like each one of them, you're blown away by it. And it just shows you how in a practical way, AI can be changing lives or doing amazing things. There's the drones there on the video. The drones, I love those, they look amazing. And then there's also the demo around using an art style and getting your picture. I'm going to get mine in a second. I think if you come by, you'll see how AI really in practice, is able to contribute to people's lives. And the vibe is awesome. And I'm loving it here. >> Well I want to say congratulations. You do amazing things. >> Thank you. >> It's really a real testament to where the society's going AI for Social Change. Microsoft has a Hackathon for Good, and this is not a one-off. I mean Microsoft certainly has had that. Google's got the 20% work on your own project. Intel has it. Companies are getting involved, a counter-culture is developing for societal benefits. And all these new things happening, like autonomous vehicles, smart cities, these are paradigm shifting society changes around the world and will require a human involvement. Congratulations, and thanks for sharing. >> Thank you very much. And we have a hashtag just for our product which is #defendhappiness. >> John: Defend happiness? >> Yeah, which is all about stopping sexual exploitation and trafficking all around the world. >> Okay, #defendhappiness. Please put it out there and share it, tweet this video. And for the betterment of society, I'm John Furrier with Federico here at the Intel AI Lounge. More coverage from South by Southwest. Three days of coverage, full day Cube today, some interviews tomorrow. Intel has some amazing super demos they're going to be showing here throughout the weekend. Stay tuned on theCUBE, we'll be covering it. We'll be right back with more, after this short break. 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