 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 Services, AWS re-invent 2017. This is theCUBE's 50 year here, been watching the progression. I'm John Furrier with Justin here, my co-host. Two next guests are Bob Rogers, the chief data scientist at Intel and Julie Cordova, who's the CEO of Thorn. Great guests showing some AI for good, Intel obviously a 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 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. Thank you. Tell your story real quick. Yeah, so Thorn is a non-profit, we started about five years ago, and we are just specifically dedicated to build new technologies to defend children from sexual abuse. We were seeing that as new technologies emerged, there was 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, live streaming 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. So you're a non-profit, and you're in that public sector, but you guys have made 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 non-profit? 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 going to 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 non-profit, 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 going to go bid for a big government RFP. 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 going to keep using it. And it kind of 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're going to accelerate congratulations. Bob, I want to get your talk take on this because it's a data problem that actually the technology is applying to a problem that people have been trying to crack the code on for a long time. Yeah, well, it's interesting because 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, it's 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 going to 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 kind of looks away because it's too hard. So I want to ask you, how do people make this happen for other areas for good? So for instance, what was the bottlenecks before? What solved the problem? Because I mean, you can 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, 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 going to do this and we're going to 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 partner 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 going to last very long. And then we built. And once we started building, we've scaled. And- So you got out in the market quickly with something. Yep, yep, 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 got to 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 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 want to 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 talks about the business logic, how she attacked that. That's really, I think that one thing notable, good use case. But from a tech perspective, how's the cloud fitting with Intel specifically? Because it really, the cloud is an enabler too. Yeah, absolutely. How's that all working with Intel, Edge? I mean, you're talking about whole new territory, you guys are forging in here. It's awesome, but cloud story. 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 their cloud is impressive. Now, I'd like to know what you guys are going to be talking about at your session. You have a session here at Reinvent. What's the title of the session? What's the agenda? Is it the same stuff here? What's going to be talked about? So we're talking about life-changing AI applications and in specific, we're going to 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 and machine learning they need to do. And then in the middle, there's a really cool deployment on AWS sandwich that MemSQL 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 I've got to ask 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. Well, if they do, well, share your strategy because cloud makes sense. I can see how you can provision easily, get in there, really empowering people to do stuff that's passionable and relevant. But how do you guys play in all this? Because 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 Thee and I, 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. Being a team player with Amazon and everyone else. Yeah, 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, 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. 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 going to invest in their infrastructure and it sort of lifts Intel's boat across the board. So it... 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 stages one with Theresa Carlson and we've been tracking her efforts and public sector's 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 in 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 though that's happening somewhere else. 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 and 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. 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. It's amazing. It happens in a situation where you have online it says 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 want to say congratulations and great work. We had a CUBE alumni founder of Cloudera, Jeff Amarbacher, good friend of the CUBE. He had a famous quote that he said on the CUBE then said on 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. We love Jeff. I read that quote all the time. It's really most important quote. Well, thanks so much. Congratulations, great inspiration, great story. Bob, thanks for coming on, appreciate it. CUBE live coverage here in 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.