>> Live from Bahrain, it's theCUBE! Covering AWS Summit Bahrain. Brought to you by Amazon Web Services. >> Okay, welcome back everyone. We are here, live in Bahrain, for the exclusive CUBE coverage of AWS Summit here in the region. Obviously, huge news, Amazon's having a region here, a full region, that's going to create a lot of connections, new opportunities, and hopefully make the life easier for all the developers and whatnot. Great guest here, so we're just talking with Kim on camera, about all the exciting developments on Amazon. We've got Abdul Raman, who's the group EVP of tech, at the ATG, which is the Al Tayyar Travel Group, in Saudi Arabia. >> Yep. >> Thanks for joining me today. >> Thanks a lot for having me. >> So, I'll quickly fast forward, you guys started in 2015, programming in the cloud, your like, we were late. I think that's actually a good time, 'cause Amazon had a lot of mature services ready. Went from zero to billions in revenue. >> Correct. >> Really big success story, that's large scale, all cloud based right? >> Yep, correct. >> Tell your story, what do you guys do, real quick, take a minute to explain your group, what you guys do, and then, what were the architectural things you decided, how did you get the growth? >> So, we are a 40 years old company, we started in 1979, we are the largest travel and tourism company in the Middle East. We went public, through our IPO in 2012. And 2015, our new board, and new management, including myself, we started building our ten-year strategy plan. And we said, we need to diversify our investment, so it mandated that we need to have an online presence. In 2015, we had a choice to build our online presence, which is very late, either on-premise using, building a data center, or we go to the cloud. We had multiple metrics including the cost efficiency, including scalability, security and so on, and all these metrics, when we compared on-premise versus cloud, cloud always win. And we selected Amazon to build our online presence. And beginning of 2015, we had zero presence, zero revenue. Our total revenue from the classic legacy systems, for the retail was almost two billion dollars. But we had zero revenue from the online. We were able, within six weeks, to build the proof of concept, and launch it immediately, and we started heavily investing in various components, from back-end, front-end, DevOps, and so on. And this year, we anticipate, we're going to be generating more than two billion riyal of revenue, that's about 450 >> Online >> Online only. >> Via cloud. >> Exactly, only on Amazon. And for us, that has been the best success story we had for years. >> It's an amazing success story actually. >> We look backward to our decision back then. >> I'll break for you, that's like actually really amazing. This is something that I think people don't really understand, what about the cloud, and certainly Amazon, and the kind of scale that you can get, if you get something right, both on the business model side and architecturally, you can be a unicorn. You're really a unicorn in revenue, that's the word that they hear in the startup world, unicorn, but mostly that's stock value, that's not actually real cash, in how many years? This is pretty phenomenal. This is the entrepreneurial dream, that is now a reality. >> Yep, that's correct. >> This is the story here. >> Exactly, and I'm happy that you mentioned that. We actually, when we started this venture, we said, to the founders, you guys are a startup. We rented out, in 2015, a garage, literally. >> Yeah, get out of the way. >> A house, A very old warehouse, we brought like, five guys, you are the core team, we told them, you are a startup, give us whatever you want to do. And it has been very successful since then. >> It's kind of like the Steve Jobs story, you got Apple, with the Mac II, and then the little group over here, you know, doing the Macintosh. >> Yep, yep, yep. >> That's your group, because you got to get out of their way, it's a mindset, I want to ask you that, that was one of my questions, but we got there a little early, but, this is a cultural shift. Cloud is a different mindset. >> Yep. >> It's not the old way of planning, team-building. >> Yep. >> It really is a different dynamic both execution wise, but team makeup. >> Correct. >> Can you share that piece of it? >> We gave our founders complete freedom, in how they're going to make up their management style. So we have a complete agile team, we have diverse geographical locations, we have people from India, developers in Egypt, in Dubai, in Saudi, and be all work and collaborate, using DevOp tools from Amazon, so we divide the work load, our product teams, weekly launch feature list. They tell us when they would like to launch every two weeks, or three weeks, a new version of the website, or the mobile apps. So, we have a completely agile development methodology, and we give our new venture a truly startup culture. >> And the key for you, if I get this right, is to have executive leadership say, we're doing this? >> Yep. >> Was that in place, did you drive that? >> Absolutely, so when our board said, told us, the new board in 2015, guys we don't have an online, go and get it, me and the CEO said, the best way to do it, is just spin off a completely different unit, completely independent, startup mentality, intro manuals, and told them, guys, sky is limit. We need to be the number one player in the Middle East. >> So, I got to dig deeper, 'cause I love, you know, it's all sexy, and great story when you say, this is how we started, and we finished strong, but as Andy Jassy would say, the CEO of AWS, the learning's in the middle, the ups and downs, as you figure things out, 'cause a lot of things about cloud, is iteration. >> Yep. >> 'Cause you have the ability to move very fast, and you get smart people together, so there's a glorious start and a glorious outcome, but in the middle is the experimentation, that's where the real work gets done. Can you share some of the learnings? Was it a technology selection? Did you really, do you have more queuing, more database, as you start to play with Amazon, this becomes, actually, a business process. >> Our biggest, yeah. >> Playing with the different pieces and which services are right for which process. Can you share something? >> Correct. So our biggest challenge was finding the right skillset, who are people who understand how Amazon, AWS, works. In the Middle East, we don't have that many skillset, or skillful people, so we had to wait, train the people, send them to Amazon workshops, be very patient with the mistakes, we don't mind people refactoring all the old code. Every month we start from scratch. We were very aware that this is, what we are doing, is never been done before in the Middle East. And what we have developed, in terms of, for example, the big data, the big data platform we build today, is one of the largest, we are processing terrabytes of data every week. It's one of the largest in the Middle East. The number of developers we have today, more than 500, working on AWS. I don't think any company in the Middle East, have that number of developers, working on this platform. So we're very proud that we gave our developers the trust and we are aware that you need to fail fast, learn, and quickly adapt. >> And it's a contagious mindset too, when you start seeing success. >> Yep. >> So talk about some of the architectural, talk about the stack that you're using. Obviously, you must be using a variety of the Amazon goodness, EC2, that's pretty obvious, are you guys using the queuing, are you using Kinesis? How you, can you talk about some of the architectural things, if you can? >> Yep, so we have, the front-end that we have today, is completely built on Node.js and AngularJS, so it's very fast, very agile. Our back end is built on Java, most of the code built on Java. We have multiple messaging buses, that asynchronous mode, so whenever there is something that needs to be given to a certain component, we don't have to wait for serial queuing. It's all parallel. At the same time, we have a lot of Auto Scaling components. One of the examples I gave earlier today, is that, we had, the beginning of this summer, we had so many marketing campaigns, and we were surprised by how successful these marketing campaigns. We have noticed, in one marketing campaign, that our demand, from our customer, have reached 300 percent, within 24 hours, and the Auto Scaling that we have in place, have been very successful. We were able to immediately meet that demand. >> Talk about how good the Auto Scaling is. Isn't that a relief? >> Absolutely. >> I mean, explain how it works because, essentially, when the demand comes in, explain how it works. >> Yep, so, just to give an example, if we had this infrastructure on-premise, we would have needed six weeks to procure a new infrastructure, install it, configure it, and we would have lost all this six weeks of revenue. >> And then, by the way, you would have lost the first 24 hour surge, then you'd go over-billed, and then wait around, and then not know if you over-provisioned. >> Absolutely. >> This is, the old way. The new way is, you configure Auto Scaling, based on policy, and then it just spins up. >> Absolutely. >> Resources. >> Absolutely. >> While you're sleeping. >> Exactly, so in a few seconds, the Auto Scaling fires up a lot of instances, and we immediately cope with the demand. >> You know, it's funny you mentioned that. One of the comments we have inside our company is, you know you're successful online, when you're making money while you're sleeping. And, you know, if you have Auto Scaling, and things of that nature, these things are programmatic, this is what elastic is all about, this is what coders, >> Yep. >> Not system administrators do >> True. >> And once they do it, they're highly motivated not to manage it again. >> Correct, absolutely. >> Again, this is back to the culture of DevOps. >> Yep, yep. >> How have you guys innovated on that piece, can you give some other examples? >> Yes, so today we have, our big data has feeds from all the buys from the big social networks, Twitter and Facebook, and also from Google, and we have all this analytical data, into our big data, and we analyze all our customer behavior, what they're looking for, what kind of destinations, holidays, business travel, and we try to adapt every two, three weeks, our product and services to meet our customer demand. Next year, we're going to be launching our machine learning, and AI infrastructure. This way, we'll be able to do real time, predictive analysis, and we will be able to serve each customer, unique, fully personalized, customized, web page and experience. We will be able to exceed our customer expectations, and we'll be able to give our customer exactly what they're looking for. >> Abdul, I got to ask you a personal question. >> Sure. >> What are you most proud of, of this success story? What are some of the things, that you look back and say, wow, we really knocked it out of the park, we did great on this, and then an example where you had a good learning experience. Maybe a trip and a fall, that was a learning opportunity. What are you most proud of? And areas that you learned the most about from, tripping and falling, and failure. >> Yep, so I think the most thing I'm proud of, is we have gathered great minds, and we have created great culture. I think great companies have great people behind them, and this, I've learned from reading the stories of Apple or Microsoft, or Google and so on. So, I think we've been very successful in this area, in the Middle East, where the resources are very scarce, and the ability to attract very smart people is very difficult, to bring them in the Middle East. And I think, we've been very successful in that regard, we've been able to gather a lot of smart people, and create great culture. >> You know, Marc Andreessen wrote that article, book about, or maybe it was a tweet, I can't even remember, the 10x engineer. >> Yep. >> And that concept is one engineer, that does cloud and DevOps right is worth ten engineers in the old world. And so, if you can collect, a selection of these 10x multipliers, that can do architecture. >> Correct. >> Now I personally believe that the full-stack developer, might be obsoleted with the cloud, or reduce the requirement for full-stack developer, but you'll still need full-stack developers for cloud, in general, but you don't need to stockpile full-stack developers. >> True, true, I agree. >> If you have good full-stack developers, you then can hire application developers >> True. >> Because the full-stack takes care of all the scale. >> Exactly, you can always repurpose those guys, and up-skill them to do something different. Instead of being a full-stack, you really want to focus on solution developer. >> Google's proven this with their SRE, if you've seen, they have operators, and developers. And this, as you scale, you're operating infrastructure, or you're writing code for applications. >> Correct. >> Alright, so what's the learnings that have been magnified for you? In the middle of the journey here, there's always the, you know, situation were, you know, you have to take care of personnel issue, or technology selection tweak or change, iteration, I won't say pivot, 'cause people don't pivot, when they're succeeding, it's just navigating through the journey. What was something that you've experienced that was magnified in the learnings, that have helped you get better? >> Yep, I believe that the multi-culture and the multi-nationalities and multi-discipline and people coming from different backgrounds. We have people from Asia, from Europe, from the U.S., in our company, and this helped having different backgrounds, different experiences, and this has helped us to build a nice, multi-dimensional solutions. And people have been able to share this experience, in a very nice way. >> That's great, Abdul, thanks so much for sharing, taking the time. >> Thank you. >> Here on theCUBE, and sharing your insight, and amazing success story, congratulations to you and your team, really love to hear these amazing success stories, essentially building from zero start, online, to billions in revenue, that's an amazing success story. >> Thank you very much for having me. >> And it certainly is great. Exclusive coverage here, we are in Bahrain, this exclusive CUBE coverage, I'm John Furrier. You can reach me on Twitter @furrier, or just search my name, reach out to me, let me know what you think. Stay with us for more coverage, after this break. (techno music fades out)

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019, brought to you by Amazon web services and along with its ecosystem partners. >>Oh, welcome back. Inside the sands, we continue our coverage here. Live coverage on the cube of AWS. Reinvent 2019. We're in day three at has been wall to wall, a lot of fun here. Tuesday, Wednesday now Thursday. Dave Volante. I'm John Walls and we're joined by Christian Rahman who was the founder and CEO of for Christian. Good morning to you. Good morning. Thanks for having afternoon. If you're watching on the, uh, on the East coast right now. Um, let's talk about sleep a little bit. I know you're all about data, um, but let's go ahead and introduce the company to those at home who might not be familiar with what your, your poor focus was. The primary focus. Absolutely. So athlete is a managed ETL as a service company. ETL is extract, transform, and load basically about getting data from different data sources, like different applications and databases into a place where it can be analyzed. >>Typically a data warehouse or a data Lake. So let's talk about the big picture then. I mean, because this has been all about data, right? I mean, accessing data, coming from the edge, coming from multiple sources, IOT, all of this, right? You had this proliferation of data and applications that come with that. Um, what are you seeing that big picture wise in terms of what people are doing with their data, how they're trying to access their data, how to turn to drive more value from it and how you serve all those masters, if you will. So there are a few trends that we see these days. One is a, you know, an obvious one that data warehouses are moving to the cloud, right? So, you know, uh, companies used to have, uh, data warehouses on premises and now they're in the cloud. They're, uh, cheaper and um, um, and more scalable, right? With services like a Redshift and snowflake in particular on AWS. Um, and then, uh, another trend is that companies have a lot more applications than they used to. You know, in the, um, in the old days you would have maybe a few data ware, sorry, databases, uh, on premises that you would integrate into your data warehouses. Nowadays you have companies have hundreds or even thousands of applications, um, that effectively become data silos, right? Where, um, uh, analysts are seeing value in that data and they want to want to have access to it. >>So, I mean, ETL is obviously not going away. I mean, it's been here forever and it'll, it'll be here forever. The challenge with ETL has always been it's cumbersome and it's expensive. It's, and now we have this new cloud era. Um, how are you guys changing ETL? >>Yeah. ETL is something that everybody would like to see go away. Everybody would just like, not to do it, but I just want to get access to their data and it should be very unfortunate for you. Right. Well, so we started, uh, we started athlete because we saw that ETL is not going away. In fact, with all the, uh, all these applications and all these needs that analysts have, it's actually becoming a bigger problem than it used to be. Um, and so, uh, what we wanted to do is basically take, take some of that pain out, right? So that companies can get to analyzing their data faster and with less engineering effort. >>Yeah. I mean, you hear this, you know, the typical story is that data scientists spend 80% of their time wrangling data and it's, and it's true in any situation. So, um, are you trying to simplify, uh, or Cloudify ETL? And if so, how are you doing that? >>So with, uh, with the growth in the number of data analysts and the number of data analytics projects that companies wants to take on the, the traditional model of having a few engineers that know how to basically make the data available for analysts, that that model is essentially now broken. And so, uh, just like you want to democratize, uh, BI and democratize analytics, you essentially have to democratize ETL as well, right? Basically that process of making the data ready for analysis. And, uh, and that is really what we're doing at athlete. We're, we're opening up ETL to a much broader audience. >>So I'm interested in how I, so I'm in pain. It's expensive. It's time consuming. Help me Christian, how, how can you help me, sir? >>So, so first of all, we're, we're, um, uh, at least specifically we're a hundred percent AWS, so we're deeply focused on, uh, Redshift data warehouses and S3 and good data lakes. Uh, and you know, there's tremendous amount of innovation. Um, those two sort of sets of technologies now, um, Redshift made a bunch of very cool announcements era at AWS reinvent this year. Um, and so what we do is we take the, uh, the infrastructure piece out, you know, so you can deploy athlete as a hosted service, uh, where we manage all the infrastructure for you or you can deploy it within your VPC. Um, again, you know, in a much, much simplified way, uh, compared to a traditional ETL technologies. Um, and then, you know, beyond that taking, uh, building pipelines, you know, building data pipelines used to be something that would take engineers six months to 18 months, something like that. But, um, but now what we, what we see is companies using athlete, they're able to do it much faster often, um, often an hours or days. >>A couple of questions there. So it's exclusively red shift, is that right? Or other analytic databases and make is >>a hundred percent AWS we're deeply focused on, on integrating well with, with AWS technologies and services. So, um, so on the data warehousing side, we support Redshift and snowflake. >>Okay, great. So I was going to ask you if snowflake was part of that. So, well you saw red shift kind of, I sort of tongue in cheek joke. They took a page out of snowflake separating compute and storage that's going to make customers very happen so they get happy. So they can scale that independently. But there's a big trend going on. I wonder if you can address it in your, you were pointing out before that there's more data sources now because of the cloud. We were just having that conversation and you're seeing the data exchange, more data sources, things like Redshift and snowflake, uh, machine intelligence, other tools like Databricks coming in at the Sage maker, a Sage maker studios, making it simpler. So it's just going to keep going faster and faster and faster, which creates opportunities for you guys. So are you seeing that trend? It's almost like a new wave of compute and workload coming into the cloud? >>Yeah, it's, it's super interesting. Companies can now access, um, a lot more data, more varied data, bigger volumes of data that they could before and um, and they want faster access to it, both in terms of the time that it takes to, you know, to, to bite zero, right? Like the time, the time that it takes to get to the first, uh, first analysis. Um, and also, um, and also in terms of the, the, the data flow itself, right? They, they not want, um, up to the second or up to the millisecond, um, uh, essentially fresh data, uh, in their dashboards and for interactive analysis. And what about the analytics side of this then when we were talking about, you know, warehousing but, but also having access to it and doing something with it. Um, what's that evolution looking like now in this new world? So lots of, um, lots of new interesting technologies there to, um, um, you know, on the, on the BI side and, um, and our focus is on, on integrating really well with the warehouses and lakes so that those, those BI tools can plug in and, and, um, um, and, and, you know, um, get access to the data straight away. Okay. >>So architecturally, why are you, uh, how are you solving the problem? Why are you able to simplify? I'm presuming it's all built in the cloud. That's been, that's kind of an obvious one. Uh, but I wonder if you could talk about that a little bit because oftentimes when we talk to companies that have started born in the cloud, John furrier has been using this notion of, you know, cloud native. Well, the meme that we've started is you take out the T it cloud native and it's cloud naive. So you're cloud native. Now what happens oftentimes with cloud native guys is much simpler, faster, lower cost, agile, you know, cloud mentality. But maybe some, sometimes it's not as functional as a company that's been around for 40 years. So you have to build that up. What's the state of ETL, you know, in your situation. Can you maybe describe that a little bit? How is it that the architecture is different and how address functionality? >>Yeah, I mean, um, so a couple of things there. Uh, um, you, you mentioned Redshift earlier and how they now announce the separation of storage and compute. I think the same is true for e-tail, right? We can, we can build on, um, on these great services that AWS develops like S three and, and, uh, a database migration service and easy to, um, elastic MapReduce, right? We can, we can take advantage of all these, all these cloud primitives and um, um, and, and so the, the infrastructure becomes operationally, uh, easier that way. Um, and, and less expensive and all, all those good things. >>You know, I wonder, Christian, if I can ask you something, given you where you live in a complicated world, I mean, data's complicated and it's getting more complicated. We heard Andy Jassy on Tuesday really give a message to the, to the enterprise. It wasn't really so much about the startups as it previously been at, at AWS reinvent. I mean, certainly talking to developers, but he, he was messaging CEOs. He had two or three CEOs on stage. But what we're describing here with, with red shift, and I threw in Databricks age maker, uh, elastic MapReduce, uh, your tooling. Uh, we just had a company on that. Does governance and, and builders have to kind of cobble these things together? Do you see an opportunity to actually create solutions for the enterprise or is that antithetical to the AWS cloud model? What, what are your thoughts? >>Oh, absolutely know them. Um, uh, these cloud services are, are fantastic primitives, but um, but enterprises clearly have a lot of, and we, we're seeing a lot of that, right? We started out in venture Bactec and, and, and got, um, a lot of, a lot of venture backed tech companies up and running quickly. But now that we're sort of moving up market and, and uh, and into the enterprise, we're seeing that they have a requirements that go way beyond, uh, beyond what, what venture tech, uh, needs. Right. And in terms of security, governance, you know, in, in ETL specifically, right? That that manifests itself in terms of, uh, not allowing data to flow out of, of the, the company's virtual private cloud for example. That's something that's very important in enterprise, a much less important than in, uh, in, in venture-backed tech. Um, data lineage. Right? That's another one. Understanding how data, uh, makes it from, you know, all those sources into the warehouse. What happens along the way. Right. And, and regulated industries in particular, that's very important. >>Yeah. I mean, I, you know, AWS is mindset is we got engineers, we're going to throw engineers at the problem and solve it. Many enterprises look at it differently. We'll pay money to save time, you know, cause we don't have the time. We don't have the resource, I feel like I, I'd like to see sort of a increasing solutions focus. Maybe it's the big SIS that provide that. Now are you guys in the marketplace today? We are. Yup. That's awesome. So how's that? How's that going? >>Yeah. Um, you mean AWS market? Yes. Yes. Uh, yeah, it's, it's um, um, that's definitely one, one channel that, uh, where there's a lot of, a lot of promise I think both. Um, for, for for enterprise companies. Yeah. >>Cause I mean, you've got to work it obviously it doesn't, just the money just doesn't start rolling in you gotta you gotta market yourselves. >>But that's definitely simplifies that, um, that model. Right? So delivering, delivering solutions to the enterprise for sure. So what's down the road for you then, uh, from, from ETL leaps perspectives here or at leaps perspectives. Um, you've talked about the complexities and what's occurred and you're not going away. ETL is here to say problems are getting bigger. What do you see the next year, 12, 18, 24 months as far as where you want to focus on? What do you think your customers are going to need you to focus on? So the big challenge, right is that, um, um, bigger and bigger companies now are realizing that there is a ton of value in their data, in all these applications, right? But in order to, in order to get value out of it, um, you have to put, uh, engineering effort today into building and maintaining these data pipelines. >>And so, uh, so yeah, so our focus is on reducing that, reducing those engineering requirements. Um, right. So that both in terms of infrastructure, pipeline, operation, pipeline setup, uh, and, and those kinds of things. So where, uh, we believe that a lot of that that's traditionally been done with specialized engineering can be done with great software. So that's, that's what we're focused on building. I love the, you know, the company tagged the perfect data pipeline. I think of like the perfect summer, the guy catching a big wave out in Maui or someplace. Good luck on catching that perfect data pipeline you guys are doing. You're solving a real problem regulations. Yeah. Good to meet you. That cause more. We are alive at AWS reinvent 2019 and you are watching the cube.

>>from Las Vegas. It's the Q covering. Next work. 2019 America's Do You buy Juniper Networks? Come back already. Jeffrey here with the Cube were in Las Vegas at Caesar's at the Juniper. Next work event. About 1000 people kind of going over a lot of new cool things. 400 gigs. Who knew that was coming out of new information for me? But that's not what we're here today. We're here for the fourth installment of around the Cube unpacking. I were happy to have all the winners of the three previous rounds here at the same place. We don't have to do it over the phone s so we're happy to have him. Let's jump into it. So winner of Round one was Bob Friday. He is the VP and CTO at Missed the Juniper Company. Bob, Great to see you. Good to be back. Absolutely. All the way from Seattle. Sharna Parky. She's a VP applied scientist at Tech CEO could see Sharna and, uh, from Google. We know a lot of a I happen to Google. Rajan's chef. He is the V p ay ay >>product management on Google. Welcome. Thank you, Christy. Here >>All right, so let's jump into it. So just warm everybody up and we'll start with you. Bob, What are some When you're talking to someone at a cocktail party Friday night talking to your mom And they say, What is a I What >>do you >>give him? A Zen examples of where a eyes of packing our lives today? >>Well, I think we all know the examples of the south driving car, you know? Aye, aye. Starting to help our health care industry being diagnosed cancer for me. Personally, I had kind of a weird experience last week at a retail technology event where basically had these new digital mirrors doing facial recognition. Right? And basically, you start to have little mirrors were gonna be a skeevy start guessing. Hey, you have a beard, you have some glasses, and they start calling >>me old. So this is kind >>of very personal. I have a something for >>you, Camille, but eh? I go walking >>down a mall with a bunch of mirrors, calling me old. >>That's a little Illinois. Did it bring you out like a cane or a walker? You know, you start getting some advertising's >>that were like Okay, you guys, this is a little bit over the top. >>Alright, Charlotte, what about you? What's your favorite example? Share with people? >>Yeah, E think one of my favorite examples of a I is, um, kind of accessible in on your phone where the photos you take on an iPhone. The photos you put in Google photos, they're automatically detecting the faces and their labeling them for you. They're like, Here's selfies. Here's your family. Here's your Children. And you know, that's the most successful one of the ones that I think people don't really think about a lot or things like getting loan applications right. We actually have a I deciding whether or not we get loans. And that one is is probably the most interesting one to be right now. >>Roger. So I think the father's example is probably my favorite as well. And what's interesting to me is that really a I is actually not about the Yeah, it's about the user experience that you can create as a result of a I. What's cool about Google photos is that and my entire family uses Google photos and they don't even know actually that the underlying in some of the most powerful a I in the world. But what they know is they confined every picture of our kids on the beach whenever they whenever they want to. Or, you know, we had a great example where we were with our kids. Every time they like something in the store, we take a picture of it, Um, and we can look up toy and actually find everything that they've taken picture. >>It's interesting because I think most people don't even know the power that they have. Because if you search for beach in your Google photos or you search for, uh, I was looking for an old bug picture from my high school there it came right up until you kind of explore. You know, it's pretty tricky, Raja, you know, I think a lot of conversation about A They always focus the general purpose general purpose, general purpose machines and robots and computers. But people don't really talk about the applied A that's happening all around. Why do you think that? >>So it's a good question. There's there's a lot more talk about kind of general purpose, but the reality of where this has an impact right now is, though, are those specific use cases. And so, for example, things like personalizing customer interaction or, ah, spotting trends that did that you wouldn't have spotted for turning unstructured data like documents into structure data. That's where a eyes actually having an impact right now. And I think it really boils down to getting to the right use cases where a I right? >>Sharon, I want ask you. You know, there's a lot of conversation. Always has A I replace people or is it an augmentation for people? And we had Gary Kasparov on a couple years ago, and he talked about, you know, it was the combination if he plus the computer made the best chess player, but that quickly went away. Now the computer is actually better than Garry Kasparov. Plus the computer. How should people think about a I as an augmentation tool versus a replacement tool? And is it just gonna be specific to the application? And how do you kind of think about those? >>Yeah, I would say >>that any application where you're making life and death decisions where you're making financial decisions that disadvantage people anything where you know you've got u A. V s and you're deciding whether or not to actually dropped the bomb like you need a human in the loop. If you're trying to change the words that you are using to get a different group of people to apply for jobs, you need a human in the loop because it turns out that for the example of beach, you type sheep into your phone and you might get just a field, a green field and a I doesn't know that, uh, you know, if it's always seen sheep in a field that when the sheep aren't there, that that isn't a sheep like it doesn't have that kind of recognition to it. So anything were we making decisions about parole or financial? Anything like that needs to have human in the loop because those types of decisions are changing fundamentally the way we live. >>Great. So shift gears. The team are Jeff Saunders. Okay, team, your mind may have been the liquid on my bell, so I'll be more active on the bell. Sorry about that. Everyone's even. We're starting a zero again, so I want to shift gears and talk about data sets. Um Bob, you're up on stage. Demo ing some some of your technology, the Miss Technology and really, you know, it's interesting combination of data sets A I and its current form needs a lot of data again. Kind of the classic Chihuahua on blue buried and photos. You got to run a lot of them through. How do you think about data sets? In terms of having the right data in a complete data set to drive an algorithm >>E. I think we all know data sets with one The tipping points for a I to become more real right along with cloud computing storage. But data is really one of the key points of making a I really write my example on stage was wine, right? Great wine starts a great grape street. Aye, aye. Starts a great data for us personally. L s t M is an example in our networking space where we have data for the last three months from our customers and rule using the last 30 days really trained these l s t m algorithms to really get that tsunami detection the point where we don't have false positives. >>How much of the training is done. Once you once you've gone through the data a couple times in a just versus when you first started, you're not really sure how it's gonna shake out in the algorithm. >>Yeah. So in our case right now, right, training happens every night. So every night, we're basically retraining those models, basically, to be able to predict if there's gonna be an anomaly or network, you know? And this is really an example. Where you looking all these other cat image thinks this is where these neural networks there really were one of the transformational things that really moved a I into the reality calling. And it's starting to impact all our different energy. Whether it's text imaging in the networking world is an example where even a I and deep learnings ruling starting to impact our networking customers. >>Sure, I want to go to you. What do you do if you don't have a big data set? You don't have a lot of pictures of chihuahuas and blackberries, and I want to apply some machine intelligence to the problem. >>I mean, so you need to have the right data set. You know, Big is a relative term on, and it depends on what you're using it for, right? So you can have a massive amount of data that represents solar flares, and then you're trying to detect some anomaly, right? If you train and I what normal is based upon a massive amount of data and you don't have enough examples of that anomaly you're trying to detect, then it's never going to say there's an anomaly there, so you actually need to over sample. You have to create a population of data that allows you to detect images you can't say, Um oh, >>I'm going to reflect in my data set the percentage of black women >>in Seattle, which is something below 6% and say it's fair. It's not right. You have to be able thio over sample things that you need, and in some ways you can get this through surveys. You can get it through, um, actually going to different sources. But you have to boot, strap it in some way, and then you have to refresh it, because if you leave that data set static like Bob mentioned like you, people are changing the way they do attacks and networks all the time, and so you may have been able to find the one yesterday. But today it's a completely different ball game >>project to you, which comes first, the chicken or the egg. You start with the data, and I say this is a ripe opportunity to apply some. Aye, aye. Or do you have some May I objectives that you want to achieve? And I got to go out and find the >>data. So I actually think what starts where it starts is the business problem you're trying to solve. And then from there, you need to have the right data. What's interesting about this is that you can actually have starting points. And so, for example, there's techniques around transfer, learning where you're able to take an an algorithm that's already been trained on a bunch of data and training a little bit further with with your data on DSO, we've seen that such that people that may have, for example, only 100 images of something, but they could use a model that's trained on millions of images and only use those 100 thio create something that's actually quite accurate. >>So that's a great segue. Wait, give me a ring on now. And it's a great Segway into talking about applying on one algorithm that was built around one data set and then applying it to a different data set. Is that appropriate? Is that correct? Is air you risking all kinds of interesting problems by taking that and applying it here, especially in light of when people are gonna go to outweigh the marketplace, is because I've got a date. A scientist. I couldn't go get one in the marketplace and apply to my data. How should people be careful not to make >>a bad decision based on that? So I think it really depends. And it depends on the type of machine learning that you're doing and what type of data you're talking about. So, for example, with images, they're they're they're well known techniques to be able to do this, but with other things, there aren't really and so it really depends. But then the other inter, the other really important thing is that no matter what at the end, you need to test and generate based on your based on your data sets and on based on sample data to see if it's accurate or not, and then that's gonna guide everything. Ultimately, >>Sharon has got to go to you. You brought up something in the preliminary rounds and about open A I and kind of this. We can't have this black box where stuff goes into the algorithm. That stuff comes out and we're not sure what the result was. Sounds really important. Is that Is that even plausible? Is it feasible? This is crazy statistics, Crazy math. You talked about the business objective that someone's trying to achieve. I go to the data scientist. Here's my data. You're telling this is the output. How kind of where's the line between the Lehman and the business person and the hard core data science to bring together the knowledge of Here's what's making the algorithm say this. >>Yeah, there's a lot of names for this, whether it's explainable. Aye, aye. Or interpret a belay. I are opening the black box. Things like that. Um, the algorithms that you use determine whether or not they're inspect herbal. Um, and the deeper your neural network gets, the harder it is to inspect, actually. Right. So, to your point, every time you take an aye aye and you use it in a different scenario than what it was built for. For example, um, there is a police precinct in New York that had a facial recognition software, and, uh, victim said, Oh, it looked like this actor. This person looked like Bill Cosby or something like that, and you were never supposed to take an image of an actor and put it in there to find people that look like them. But that's how people were using it. So the Russians point yes, like it. You can transfer learning to other a eyes, but it's actually the humans that are using it in ways that are unintended that we have to be more careful about, right? Um, even if you're a, I is explainable, and somebody tries to use it in a way that it was never intended to be used. The risk is much higher >>now. I think maybe I had, You know, if you look at Marvis kind of what we're building for the networking community Ah, good examples. When Marvis tries to do estimate your throughput right, your Internet throughput. That's what we usually call decision tree algorithm. And that's a very interpretive algorithm. and we predict low throughput. We know how we got to that answer, right? We know what features God, is there? No. But when we're doing something like a NAMI detection, that's a neural network. That black box it tells us yes, there's a problem. There's some anomaly, but that doesn't know what caused the anomaly. But that's a case where we actually used neural networks, actually find the anomie, and then we're using something else to find the root cause, eh? So it really depends on the use case and where the night you're going to use an interpreter of model or a neural network which is more of a black box model. T tell her you've got a cat or you've got a problem >>somewhere. So, Bob, that's really interested. So can you not unpacking? Neural network is just the nature of the way that the communication and the data flows and the inferences are made that you can't go in and unpack it, that you have to have the >>separate kind of process too. Get to the root cause. >>Yeah, assigned is always hard to say. Never. But inherently s neural networks are very complicated. Saito set of weights, right? It's basically usually a supervised training model, and we're feeding a bunch of data and trying to train it to detect a certain features, sir, an output. But that is where they're powerful, right? And that's why they basically doing such good, Because they are mimicking the brain, right? That neural network is a very complex thing. Can't like your brain, right? We really don't understand how your brain works right now when you have a problem, it's really trialling there. We try to figure out >>right going right. So I want to stay with you, bought for a minute. So what about when you change what you're optimizing? Four? So you just said you're optimizing for throughput of the network. You're looking for problems. Now, let's just say it's, uh, into the end of the quarter. Some other reason we're not. You're changing your changing what you're optimizing for, Can you? You have to write separate algorithm. Can you have dynamic movement inside that algorithm? How do you approach a problem? Because you're not always optimizing for the same things, depending on the market conditions. >>Yeah, I mean, I think a good example, you know, again, with Marvis is really with what we call reinforcement. Learning right in reinforcement. Learning is a model we use for, like, radio resource management. And there were really trying to optimize for the user experience in trying to balance the reward, the models trying to reward whether or not we have a good balance between the network and the user. Right, that reward could be changed. So that algorithm is basically reinforcement. You can finally change hell that Algren works by changing the reward you give the algorithm >>great. Um, Rajan back to you. A couple of huge things that have come into into play in the marketplace and get your take one is open source, you know, kind of. What's the impact of open source generally on the availability, desire and more applications and then to cloud and soon to be edge? You know, the current next stop. How do you guys incorporate that opportunity? How does it change what you can do? How does it open up the lens of >>a I Yeah, I think open source is really important because I think one thing that's interesting about a I is that it's a very nascent field and the more that there's open source, the more that people could build on top of each other and be able to utilize what what others others have done. And it's similar to how we've seen open source impact operating systems, the Internet, things like things like that with Cloud. I think one of the big things with cloud is now you have the processing power and the ability to access lots of data to be able to t create these thes networks. And so the capacity for data and the capacity for compute is much higher. Edge is gonna be a very important thing, especially going into next few years. You're seeing Maur things incorporated on the edge and one exciting development is around Federated learning where you can train on the edge and then combine some of those aspects into a cloud side model. And so that I think will actually make EJ even more powerful. >>But it's got to be so dynamic, right? Because the fundamental problem used to always be the move, the computer, the data or the date of the computer. Well, now you've got on these edge devices. You've got Tanya data right sensor data all kinds of machining data. You've got potentially nasty hostile conditions. You're not in a nice, pristine data center where the environmental conditions are in the connective ity issues. So when you think about that problem yet, there's still great information. There you got latent issues. Some I might have to be processed close to home. How do you incorporate that age old thing of the speed of light to still break the break up? The problem to give you a step up? Well, we see a lot >>of customers do is they do a lot of training on the cloud, but then inference on the on the edge. And so that way they're able to create the model that they want. But then they get fast response time by moving the model to the edge. The other thing is that, like you said, lots of data is coming into the edge. So one way to do it is to efficiently move that to the cloud. But the other way to do is filter. And to try to figure out what data you want to send to the clouds that you can create the next days. >>Shawna, back to you let's shift gears into ethics. This pesky, pesky issue that's not not a technological issue at all, but right. We see it often, especially in tech. Just cause you should just cause you can doesn't mean that you should. Um so and this is not a stem issue, right? There's a lot of different things that happened. So how should people be thinking about ethics? How should they incorporate ethics? Um, how should they make sure that they've got kind of a, you know, a standard kind of overlooking kind of what they're doing? The decisions are being made. >>Yeah, One of the more approachable ways that I have found to explain this is with behavioral science methodologies. So ethics is a massive field of study, and not everyone shares the same ethics. However, if you try and bring it closer to behavior change because every product that we're building is seeking to change of behavior. We need to ask questions like, What is the gap between the person's intention and the goal we have for them? Would they choose that goal for themselves or not? If they wouldn't, then you have an ethical problem, right? And this this can be true of the intention, goal gap or the intention action up. We can see when we regulated for cigarettes. What? We can't just make it look cool without telling them what the cigarettes are doing to them, right so we can apply the same principles moving forward. And they're pretty accessible without having to know. Oh, this philosopher and that philosopher in this ethicist said these things, it can be pretty human. The challenge with this is that most people building these algorithms are not. They're not trained in this way of thinking, and especially when you're working at a start up right, you don't have access to massive teams of people to guide you down this journey, so you need to build it in from the beginning, and you need to be open and based upon principles. Um, and it's going to touch every component. It should touch your data, your algorithm, the people that you're using to build the product. If you only have white men building the product, you have a problem you need to pull in other people. Otherwise, there are just blind spots that you are not going to think of in order to still that product for a wider audience, but it seems like >>they were on such a razor sharp edge. Right with Coca Cola wants you to buy Coca Cola and they show ads for Coca Cola, and they appeal to your let's all sing together on the hillside and be one right. But it feels like with a I that that is now you can cheat. Right now you can use behavioral biases that are hardwired into my brain is a biological creature against me. And so where is where is the fine line between just trying to get you to buy Coke? Which somewhat argues Probably Justus Bad is Jule cause you get diabetes and all these other issues, but that's acceptable. But cigarettes are not. And now we're seeing this stuff on Facebook with, you know, they're coming out. So >>we know that this is that and Coke isn't just selling Coke anymore. They're also selling vitamin water so they're they're play isn't to have a single product that you can purchase, but it is to have a suite of products that if you weren't that coke, you can buy it. But if you want that vitamin water you can have that >>shouldn't get vitamin water and a smile that only comes with the coat. Five. You want to jump in? >>I think we're going to see ethics really break into two different discussions, right? I mean, ethics is already, like human behavior that you're already doing right, doing bad behavior, like discriminatory hiring, training, that behavior. And today I is gonna be wrong. It's wrong in the human world is gonna be wrong in the eye world. I think the other component to this ethics discussion is really round privacy and data. It's like that mirror example, right? No. Who gave that mirror the right to basically tell me I'm old and actually do something with that data right now. Is that my data? Or is that the mirrors data that basically recognized me and basically did something with it? Right. You know, that's the Facebook. For example. When I get the email, tell me, look at that picture and someone's take me in the pictures Like, where was that? Where did that come from? Right? >>What? I'm curious about to fall upon that as social norms change. We talked about it a little bit for we turn the cameras on, right? It used to be okay. Toe have no black people drinking out of a fountain or coming in the side door of a restaurant. Not that long ago, right in the 60. So if someone had built an algorithm, then that would have incorporated probably that social norm. But social norms change. So how should we, you know, kind of try to stay ahead of that or at least go back reflectively after the fact and say kind of back to the black box, That's no longer acceptable. We need to tweak this. I >>would have said in that example, that was wrong. 50 years ago. >>Okay, it was wrong. But if you ask somebody in Alabama, you know, at the University of Alabama, Matt Department who have been born Red born, bred in that culture as well, they probably would have not necessarily agreed. But so generally, though, again, assuming things change, how should we make sure to go back and make sure that we're not again carrying four things that are no longer the right thing to do? >>Well, I think I mean, as I said, I think you know what? What we know is wrong, you know is gonna be wrong in the eye world. I think the more subtle thing is when we start relying on these Aye. Aye. To make decisions like no shit in my car, hit the pedestrian or save my life. You know, those are tough decisions to let a machine take off or your balls decision. Right when we start letting the machines Or is it okay for Marvis to give this D I ps preference over other people, right? You know, those type of decisions are kind of the ethical decision, you know, whether right or wrong, the human world, I think the same thing will apply in the eye world. I do think it will start to see more regulation. Just like we see regulation happen in our hiring. No, that regulation is going to be applied into our A I >>right solutions. We're gonna come back to regulation a minute. But, Roger, I want to follow up with you in your earlier session. You you made an interesting comment. You said, you know, 10% is clearly, you know, good. 10% is clearly bad, but it's a soft, squishy middle at 80% that aren't necessarily super clear, good or bad. So how should people, you know, kind of make judgments in this this big gray area in the middle? >>Yeah, and I think that is the toughest part. And so the approach that we've taken is to set us set out a set of AI ai principles on DDE. What we did is actually wrote down seven things that we will that we think I should do and four things that we should not do that we will not do. And we now have to actually look at everything that we're doing against those Aye aye principles. And so part of that is coming up with that governance process because ultimately it boils down to doing this over and over, seeing lots of cases and figuring out what what you should do and so that governments process is something we're doing. But I think it's something that every company is going to need to do. >>Sharon, I want to come back to you, so we'll shift gears to talk a little bit about about law. We've all seen Zuckerberg, unfortunately for him has been, you know, stuck in these congressional hearings over and over and over again. A little bit of a deer in a headlight. You made an interesting comment on your prior show that he's almost like he's asking for regulation. You know, he stumbled into some really big Harry nasty areas that were never necessarily intended when they launched Facebook out of his dorm room many, many moons ago. So what is the role of the law? Because the other thing that we've seen, unfortunately, a lot of those hearings is a lot of our elected officials are way, way, way behind there, still printing their e mails, right? So what is the role of the law? How should we think about it? What shall we What should we invite from fromthe law to help sort some of this stuff out? >>I think as an individual, right, I would like for each company not to make up their own set of principles. I would like to have a shared set of principles that were following the challenge. Right, is that with between governments, that's impossible. China is never gonna come up with same regulations that we will. They have a different privacy standards than we D'oh. Um, but we are seeing locally like the state of Washington has created a future of work task force. And they're coming into the private sector and asking companies like text you and like Google and Microsoft to actually advise them on what should we be regulating? We don't know. We're not the technologists, but they know how to regulate. And they know how to move policies through the government. What will find us if we don't advise regulators on what we should be regulating? They're going to regulate it in some way, just like they regulated the tobacco industry. Just like they regulated. Sort of, um, monopolies that tech is big enough. Now there is enough money in it now that it will be regularly. So we need to start advising them on what we should regulate because just like Mark, he said. While everyone else was doing it, my competitors were doing it. So if you >>don't want me to do it, make us all stop. What >>can I do? A negative bell and that would not for you, but for Mark's responsibly. That's crazy. So So bob old man at the mall. It's actually a little bit more codified right, There's GDP are which came through May of last year and now the newness to California Extra Gatorade, California Consumer Protection Act, which goes into effect January 1. And you know it's interesting is that the hardest part of the implementation of that I think I haven't implemented it is the right to be for gotten because, as we all know, computers, air, really good recording information and cloud. It's recorded everywhere. There's no there there. So when these types of regulations, how does that impact? Aye, aye, because if I've got an algorithm built on a data set in in person, you know, item number 472 decides they want to be forgotten How that too I deal with that. >>Well, I mean, I think with Facebook, I can see that as I think. I suspect Mark knows what's right and wrong. He's just kicking ball down tires like >>I want you guys. >>It's your problem, you know. Please tell me what to do. I see a ice kind of like any other new technology, you know, it could be abused and used in the wrong waste. I think legally we have a constitution that protects our rights. And I think we're going to see the lawyers treat a I just like any other constitutional things and people who are building products using a I just like me build medical products or other products and actually harmful people. You're gonna have to make sure that you're a I product does not harm people. You're a product does not include no promote discriminatory results. So I >>think we're going >>to see our constitutional thing is going applied A I just like we've seen other technologies work. >>And it's gonna create jobs because of that, right? Because >>it will be a whole new set of lawyers >>the holdings of lawyers and testers, even because otherwise of an individual company is saying. But we tested. It >>works. Trust us. Like, how are you gonna get the independent third party verification of that? So we're gonna start to see a whole terrorist proliferation of that type of fields that never had to exist before. >>Yeah, one of my favorite doctor room. A child. Grief from a center. If you don't follow her on Twitter Follower. She's fantastic and a great lady. So I want to stick with you for a minute, Bob, because the next topic is autonomous. And Rahman up on the keynote this morning, talked about missed and and really, this kind of shifting workload of fixing things into an autonomous set up where the system now is, is finding problems, diagnosing problems, fixing problems up to, I think, he said, even generating return authorizations for broken gear, which is amazing. But autonomy opens up all kinds of crazy, scary things. Robert Gates, we interviewed said, You know, the only guns that are that are autonomous in the entire U. S. Military are the ones on the border of North Korea. Every single other one has to run through a person when you think about autonomy and when you can actually grant this this a I the autonomy of the agency toe act. What are some of the things to think about in the word of the things to keep from just doing something bad, really, really fast and efficiently? >>Yeah. I mean, I think that what we discussed, right? I mean, I think Pakal purposes we're far, you know, there is a tipping point. I think eventually we will get to the CP 30 Terminator day where we actually build something is on par with the human. But for the purposes right now, we're really looking at tools that we're going to help businesses, doctors, self driving cars and those tools are gonna be used by our customers to basically allow them to do more productive things with their time. You know, whether it's doctor that's using a tool to actually use a I to predict help bank better predictions. They're still gonna be a human involved, you know, And what Romney talked about this morning and networking is really allowing our I T customers focus more on their business problems where they don't have to spend their time finding bad hard were bad software and making better experiences for the people. They're actually trying to serve >>right, trying to get your take on on autonomy because because it's a different level of trust that we're giving to the machine when we actually let it do things based on its own. But >>there's there's a lot that goes into this decision of whether or not to allow autonomy. There's an example I read. There's a book that just came out. Oh, what's the title? You look like a thing. And I love you. It was a book named by an A I, um if you want to learn a lot about a I, um and you don't know much about it, Get it? It's really funny. Um, so in there there is in China. Ah, factory where the Aye Aye. Is optimizing um, output of cockroaches now they just They want more cockroaches now. Why do they want that? They want to grind them up and put them in a lotion. It's one of their secret ingredients now. It depends on what parameters you allow that I to change, right? If you decide Thio let the way I flood the container, and then the cockroaches get out through the vents and then they get to the kitchen to get food, and then they reproduce the parameters in which you let them be autonomous. Over is the challenge. So when we're working with very narrow Ai ai, when use hell the Aye. Aye. You can change these three things and you can't just change anything. Then it's a lot easier to make that autonomous decision. Um and then the last part of it is that you want to know what is the results of a negative outcome, right? There was the result of a positive outcome. And are those results something that we can take actually? >>Right, Right. Roger, don't give you the last word on the time. Because kind of the next order of step is where that machines actually write their own algorithms, right? They start to write their own code, so they kind of take this next order of thought and agency, if you will. How do you guys think about that? You guys are way out ahead in the space, you have huge data set. You got great technology. Got tensorflow. When will the machines start writing their own A their own out rhythms? Well, and actually >>it's already starting there that, you know, for example, we have we have a product called Google Cloud. Ottawa. Mel Village basically takes in a data set, and then we find the best model to be able to match that data set. And so things like that that that are there already, but it's still very nascent. There's a lot more than that that can happen. And I think ultimately with with how it's used I think part of it is you have to start. Always look at the downside of automation. And what is what is the downside of a bad decision, whether it's the wrong algorithm that you create or a bad decision in that model? And so if the downside is really big, that's where you need to start to apply Human in the loop. And so, for example, in medicine. Hey, I could do amazing things to detect diseases, but you would want a doctor in the loop to be able to actually diagnose. And so you need tohave have that place in many situations to make sure that it's being applied well. >>But is that just today? Or is that tomorrow? Because, you know, with with exponential growth and and as fast as these things are growing, will there be a day where you don't necessarily need maybe need the doctor to communicate the news? Maybe there's some second order impacts in terms of how you deal with the family and, you know, kind of pros and cons of treatment options that are more emotional than necessarily mechanical, because it seems like eventually that the doctor has a role. But it isn't necessarily in accurately diagnosing a problem. >>I think >>I think for some things, absolutely over time the algorithms will get better and better, and you can rely on them and trust them more and more. But again, I think you have to look at the downside consequence that if there's a bad decision, what happens and how is that compared to what happens today? And so that's really where, where that is. So, for example, self driving cars, we will get to the point where cars are driving by themselves. There will be accidents, but the accident rate is gonna be much lower than what's there with humans today, and so that will get there. But it will take time. >>And there was a day when will be illegal for you to drive. You have manslaughter, right? >>I I believe absolutely there will be in and and I don't think it's that far off. Actually, >>wait for the day when I have my car take me up to Northern California with me. Sleepy. I've only lived that long. >>That's right. And work while you're while you're sleeping, right? Well, I want to thank everybody Aton for being on this panel. This has been super fun and these air really big issues. So I want to give you the final word will just give everyone kind of a final say and I just want to throw out their Mars law. People talk about Moore's law all the time. But tomorrow's law, which Gardner stolen made into the hype cycle, you know, is that we tend to overestimate in the short term, which is why you get the hype cycle and we turn. Tend to underestimate, in the long term the impacts of technology. So I just want it is you look forward in the future won't put a year number on it, you know, kind of. How do you see this rolling out? What do you excited about? What are you scared about? What should we be thinking about? We'll start with you, Bob. >>Yeah, you know, for me and, you know, the day of the terminus Heathrow. I don't know if it's 100 years or 1000 years. That day is coming. We will eventually build something that's in part of the human. I think the mission about the book, you know, you look like a thing and I love >>you. >>Type of thing that was written by someone who tried to train a I to basically pick up lines. Right? Cheesy pickup lines. Yeah, I'm not for sure. I'm gonna trust a I to help me in my pickup lines yet. You know I love you. Look at your thing. I love you. I don't know if they work. >>Yeah, but who would? Who would have guessed online dating is is what it is if you had asked, you know, 15 years ago. But I >>think yes, I think overall, yes, we will see the Terminator Cp through It was probably not in our lifetime, but it is in the future somewhere. A. I is definitely gonna be on par with the Internet cell phone, radio. It's gonna be a technology that's gonna be accelerating if you look where technology's been over last. Is this amazing to watch how fast things have changed in our lifetime alone, right? Yeah, we're just on this curve of technology accelerations. This in the >>exponential curves China. >>Yeah, I think the thing I'm most excited about for a I right now is the addition of creativity to a lot of our jobs. So ah, lot of we build an augmented writing product. And what we do is we look at the words that have happened in the world and their outcomes. And we tell you what words have impacted people in the past. Now, with that information, when you augment humans in that way, they get to be more creative. They get to use language that have never been used before. To communicate an idea. You can do this with any field you can do with composition of music. You can if you can have access as an individual, thio the data of a bunch of cultures the way that we evolved can change. So I'm most excited about that. I think I'm most concerned currently about the products that we're building Thio Give a I to people that don't understand how to use it or how to make sure they're making an ethical decision. So it is extremely easy right now to go on the Internet to build a model on a data set. And I'm not a specialist in data, right? And so I have no idea if I'm adding bias in or not, um and so it's It's an interesting time because we're in that middle area. Um, and >>it's getting loud, all right, Roger will throw with you before we have to cut out, or we're not gonna be able to hear anything. So I actually start every presentation out with a picture of the Mosaic browser, because what's interesting is I think that's where >>a eyes today compared to kind of weather when the Internet was around 1994 >>were just starting to see how a I can actually impact the average person. As a result, there's a lot of hype, but what I'm actually finding is that 70% of the company's I talked to the first question is, Why should I be using this? And what benefit does it give me? Why 70% ask you why? Yeah, and and what's interesting with that is that I think people are still trying to figure out what is this stuff good for? But to your point about the long >>run, and we underestimate the longer I think that every company out there and every product will be fundamentally transformed by eye over the course of the next decade, and it's actually gonna have a bigger impact on the Internet itself. And so that's really what we have to look forward to. >>All right again. Thank you everybody for participating. There was a ton of fun. Hope you had fun. And I look at the score sheet here. We've got Bob coming in and the bronze at 15 points. Rajan, it's 17 in our gold medal winner for the silver Bell. Is Sharna at 20 points. Again. Thank you. Uh, thank you so much and look forward to our next conversation. Thank Jeffrey Ake signing out from Caesar's Juniper. Next word unpacking. I Thanks for watching.

>> live from Washington D. C. It's the Cube covering a ws public sector summit by Amazon Web services. >> Hello, everyone. Welcome back to the cubes Live coverage of a ws Public Sector summit here in our nation's capital. I'm your host, Rebecca Knight. We're joined by Raghu Rahman. He is the director of Fin Row, the Financial Industry Regulatory Authority. Thank you so much for coming on the Cube >> fighter back. Good afternoon, but happy to be here. >> So we're angry. This is the 10th annual public sector. Somebody should have said so Tell us a little bit about Finn Ra and what you do. They're >> sure Fender itself is the financial industry Regulatory authority way our private sector, not for profit institutions. Our mission is investor protection on market integrity. Way our member funded on DH. We have a member driven board board of directors and we engage in ensuring that all the stock market operations in the U. S. Capital markets play with rules. So that's the essence of who we are. >> And all of those stakeholders have a vested interest in making sure their rivals are also playing bythe. So you're here giving a presentation on fraud detection, using machine learning and artificial intelligence. That's right. What was So what were you saying? >> So, Brenda, we have a very deliberate technology strategy on We constantly keep pace with technology in order to affect our business in the best possible way, way. Always are looking for a means to get more efficient and more effective and use our funding for the best possible business value so to that, and wear completely in the cloud for a lot off our market regulation operations. All the applications are in the clouds. We, in fact, we were one of the early adopters of the cloud. From that perspective, all of our big data operations were fully operational in the cloud by 2016 itself. That was itself a two year project that we started in 40 14 then from 2016 were being working with machine language on recently. Over the past six months or so, we've been working with neural networks. So this was an opportunity for us to share what? Where we have bean, where we're coming from, where we're going with the intent that whatever we do by way of principles can be adopted by any other enterprise. We're looking to share our journey on to encourage others to adopt technology. That's really what why we do this >> and I want to dig into the presentation a little bit. But can you just set the scene for our viewers about what kinds of how big a problem fraud is with these financial institutions and how much money is on the table here? >> Well, I don't want to get you to the actual dollar figures, because each dimension off it comes up with a different aspect to it. Waken say that in full in federal, we have a full caseload year after year, decade after decade that end up with multiple millions of dollars worth of fines just on the civil cases alone. And then there are, of course, multibillion dollar worth problems that we read in the media cases going as far back as Bernie Madoff. Case is going through the different banking systems so that our various kinds of fraud across the different financial sectors, of course, we're focused on the capital markets alone. We don't do anything with regard to banking or things of that nature, But even in our own case, we franchise composed of nearly 33 100 people on all of us, engaging the fulltime task of ensuring that markets are fair for the investors on for the other participants, it's a big deal. >> So in your in your presentation, you told the story of two of your colleagues who are facing different kinds of challenges to sort to make your story come alive. Tell our viewers a little bit about about their challenges. >> We spoke about Brad, who is an expert. He's an absolute wizard when it comes to market regulation, and he's being doing this for a long time on DH What I shared with the members of the audience earlier today. Wass He can probably look ATT market, even data on probably tell you what the broker had for breakfast. >> That >> scary good on. We also shared the story about Jamie, who is in the member supervision division offender, a wicked, smart and extensive experience. So these are the kind of dedicated people that we have a fender on guy took up to Rhea life use cases sort of questions that they face. So in the case of Brad, it is always a question of Hey, we're good. But how do we get better? What is the unknown unknown there? The volume of transactions in the market keeps going up. How do we then end up with a situation where we can do effective surveillance in the market on detect the behaviors that are not off interest that are not for doctor? That might be even. Don't write manipulated. How do we make sure that way? Got it all, so to speak? That's Brad's thing. >> That idea about these? No, these unknown nun note Because we know we have no no known unknowns with the unknown unknowns are even scarier. >> Exactly. They are, and we want to shed light on that for ourselves and make sure that the markets are really fair for everybody to operate him. That is where use of the latest technologies helps us get better and better at it. To reduce the number of unknown unknowns to shed light on the entirety of market activities on toe, perform effective surveillance. So that was a just off our conversation today. How we have gotten better in the past 45 years, how machine language machine learning based technologies have helped us how artificial intelligence that we started working with specifically, neural networks have started helping us even further. >> Okay, okay. And then Jamie had a problem, too. >> In Jimmy's case. Member supervision, if you will. The problem is off a different context and character. They're still volumes of data. We still receive more than 1,000,000 individual pieces of document every year that we work with. But in her case, the important aspect of it is that it is unstructured data. It makes sense to humans. It is in plain English, but the machines, it's really difficult. So over the past two years, way have created an entirely new text analytics platform on that helps us parts through hundreds of thousands of different documents. Those could come from e mails it to come from war documents, spreadsheets, evenhanded and documents. We can go through all of those extract meaningful information, automatically summarized them, even have measures off confidence that the machine will imprint upon it to say how confident I am. I that this is off relevance to you. It will imprint that. And then it represented Jamie for her toe. Use her judgment and expertise to make a final call. One thing that we are really conscious about is way. Don't let algorithms completely take everything through. We always have a human. So we think of a I as really assistive intelligence on. We bring that to a fact for our business, >> and I think that that's a really key there, too, for the for the employees is to know that this is this is this's taking away some of their more manual, more boring tests and actually freeing them up to do the more creative, analytical problem solving >> you hit you. I think you hit that nail right on the head. All the tedious work the machine bus on. Then it leaves humans to do like you said, Absolutely the creative, the inter toe on the final judgment call. I think that's a great system. >> How much to these solutions cost way >> generally are not pricing these things individually, however overall, one of the things that we did with the cloud was actually reduce our overall cost ofthe technology. So from that perspective, we don't look at Costas, the primary driver, although many times these things do end up costing less than the prior system that we would be in. However, the benefits that offer to our clientele, the benefit that it offers to our business, to the people that are investors in the stock market, that is tremendous, and that has a lot of value for us. >> So what is next for Finneran? I mean, this is This is a really moment for so many industries in terms of the the rise of cyber threats, the end and fraud being such a huge problem. Privacy thes air the financial services industry more than, I guess maybe is equal to healthcare. This's really sensitive stuff we're talking about here. What what are some of the things that you have on the horizon? What are some of the things that you're hearing from your members? >> So all of our members treat data security really, really special on really carefully on wear, very deliberate and very conscious about how we treat the data that is interested to us way have to obligations. One is to treat it securely. The other is to extract appropriate insights from it because that's the purpose of why we're being interested with the data. Wait, take both of those dimensions very seriously. Way have an entire infrastructure organization. It's composed off experts in the field way, headed by a chief information security officer with a large team that looks at multi layered security right from the application defending itself all the way to perimeter security. We go off that we have extensive identity and access management systems. We also have an extensive program to combat insider tracks. So this type of multi layer security is what helps us keep the data secure. >> And >> every day we do notice that there are additional track factors that get exposed. So we keep ourselves on the edge in terms ofthe working with all the vendors that we partner with in working with the latest technologies to protect our data as an example, all of our data in the cloud is completely encrypted with high encryption, and it is encrypted both at rest. I'm during flight so that even in the rare case that someone has access to something is gibberish. So that's the intent of the encryption himself. So that is the extent to which we take things very seriously. >> I want to ask you to, but the technology backlash that we're seeing so much and you're you live here so you really know about the climate that does that technology industries, air facing for so long. They were our national treasure and they still are considered it all in a lot of ways. The Amazons, the Googles, the facebooks of the world. But now we have a presidential candidates calling for the break up of big tech and and they And there's been a real souring on the part of the public of concerns about privacy. How What are your thoughts? What are you seeing? What are you hearing on the ground here in D. C? >> With specifically with regard to where we operate from Infanta? We've tried not to access or use any data. That is not for regulatory purpose. Wear Very careful about it. Way don't sprawl across and crawl across social media just on a general fishing expedition. We try not to do that. All of the data that we take in store on operate technology upon we are entitled to use it for by policy are my rules are my regulation for the specific purpose off our regulator activities. We take that very seriously. We try not to access data outside off what we have need for on. So we limit ourselves to the context and that, if you look at, is really what the public is trying to tell us, don't take our data and use it in ways that we did not really authorize you to do. So So the other thing is that franchise on our profit, not for not for profit institutions. We really have absolutely no interest beyond regulatory capability to use the data. We absolutely shut it down for any other use way are not so that way. We are very clear about what our mission is. Where we use our data, why we use it and stop. >> Great. Well, Raghu, thank you so much for coming on the Cube. It's been a pleasure talking to you. >> Thank you. Thank >> you. I'm Rebecca Knight. Please stay tuned for more of the cubes. Live coverage of the es W s public Sector summit here in Washington. D c. Stay tuned. >> Oh,