Placeholder Image

字幕表 動画を再生する

  • >> Narrator: Live from Las Vegas, it's theCUBE,

  • covering AWS re:Invent 2017, presented by AWS,

  • Intel, and our ecosystem of partners.

  • >> Hello everyone, welcome to a special

  • CUBE presentation here, live in Las Vegas for

  • Amazon Web Service's AWS re:Invent 2017.

  • This is theCUBE's fifth year here.

  • We've been watching the progression.

  • I'm John Furrier with Justin here as my co-host.

  • Our two next guests are Bob Rogers,

  • the chief data scientist at Intel,

  • and Julie Cardoa, who's the CEO of Thorn.

  • Great guests, showing some AI for good.

  • Intel, obviously, good citizen and great technology partner.

  • Welcome to theCUBE.

  • >> Thank you, thanks for having us!

  • >> So, I saw your talk you gave at

  • the Public Sector Breakfast this morning

  • here at re:Invent.

  • Packed house, fire marshal was kicking people out.

  • Really inspirational story.

  • Intel, we've talked at South by Southwest.

  • You guys are really doing a lot of AI for good.

  • That's the theme here.

  • You guys are doing incredible work.

  • >> Julie: Thank you.

  • >> Tell your story real quick.

  • >> Yeah, so Thorn is a nonprofit,

  • we started about five years ago,

  • and we are just specifically dedicated to build

  • new technologies to defend children form sexual abuse.

  • We were seeing that, as, you know,

  • new technologies emerge, there's new innovation out there,

  • how child sexual abuse was presenting itself

  • was changing dramatically.

  • So, everything from child sex trafficking online,

  • to the spread of child sexual abuse material,

  • livestreaming abuse, and there wasn't a concentrated effort

  • to put the best and brightest minds and technology together

  • to be a part of the solution,

  • and so that's what we do.

  • We build products to stop child abuse.

  • >> John: So you're a nonprofit?

  • >> Julie: Yep!

  • >> And you're in that public sector,

  • but you guys have made a great progress.

  • What's the story behind it?

  • How did you get to do so effective work

  • in such a short period of time as a nonprofit?

  • >> Well, I think there's a couple things to that.

  • One is, well, we learned a lot really quickly,

  • so what we're doing today is not what

  • we thought we would do five years ago.

  • We thought we were gonna talk to big companies,

  • and push them to do more,

  • and then we realized that we actually needed to be a hub.

  • We needed to build our own engineering teams,

  • we needed to build product,

  • and then bring in these companies to help us,

  • and to add to that,

  • but there had to be some there there,

  • and so we actually have evolved.

  • We're a nonprofit, but we are a product company.

  • We have two products used in 23 countries around the world,

  • stopping abuse every day.

  • And I think the other thing we learned is that

  • we really have to break down silos.

  • So, we didn't, in a lot of our development,

  • we didn't go the normal route of saying,

  • okay, well this is a law enforcement job,

  • so we're gonna go bid for a big government RFE.

  • We just went and built a tool and gave it

  • to a bunch of police officers and they said,

  • "Wow, this works really well,

  • "we're gonna keep using it."

  • And it kinda spread like wildfire.

  • >> And it's making a difference.

  • It's really been a great inspirational story.

  • Check out Thorn, amazing work,

  • real use case, in my mind, a testimonial for

  • how fast you can accelerate.

  • Congratulations.

  • Bob, I wanna get your take on this because

  • it's a data problem that, actually,

  • the technology's applying to a problem that

  • people have been trying to

  • crack the code on for a long time.

  • >> Yeah, well, it's interesting,

  • 'cause the context is that we're really in this

  • era of AI explosion,

  • and AI is really computer systems that can do things

  • that only humans could do 10 years ago.

  • That's kind of my basic way of thinking about it,

  • so the problem of being able to recognize

  • when you're looking at two images of the same child,

  • which is the piece that we solved for Thorn,

  • actually, you know, is a great example of using

  • the current AI capabilities.

  • You start with the problem of,

  • if I show an algorithm two different images

  • of the same child, can it recognize that they're the same?

  • And you basically customize your training

  • to create a very specific capability.

  • Not a basic image recognition or facial recognition,

  • but a very specific capability

  • that's been trained with specific examples.

  • I was gonna say something about what

  • Julie was describing about their model.

  • Their model to create that there there

  • has been incredible because

  • it allows them to really focus our energy

  • into the right problems.

  • We have lots of technology,

  • we have lots of different ways of

  • doing AI and machine learning,

  • but when we get a focus on this is the data,

  • this is the exact problem we need to solve,

  • and this is the way it needs to work for law enforcement,

  • for National Center for Missing and Exploited Children.

  • It has really just turned the knob up to 11, so to speak.

  • >> I mean, this is an example where, I mean,

  • we always talk about how tech transformation

  • can make things go faster.

  • It's such an obvious problem.

  • I mean, it's almost everyone kinda looks away

  • because it's too hard.

  • So, I wanna ask you, how do people make this happen

  • for other areas for good?

  • So, for instance, you know,

  • what was the bottlenecks before?

  • What solved the problem, because, I mean,

  • you could really make a difference here.

  • You guys are.

  • >> Well, I think there's a couple things.

  • I think you hit on one, which is

  • this is a problem people turn away from.

  • It's really hard to look at.

  • And the other thing is is

  • there's not a lot of money to be made

  • in using advanced technology to find

  • missing and exploited children, right?

  • So, it did require the development

  • of a nonprofit that said, "We're gonna do this,

  • "and we're gonna fundraise to get it done."

  • But it also required us to look at it

  • from a technology angle, right?

  • I think a lot of times people look at social issues

  • from the impact angle, which we do,

  • but we said, "What if we looked at it

  • "from a different perspective?

  • "How can technology disrupt in this area?"

  • And then we made that the core of what we do,

  • and we partnered with all the other

  • amazing organizations that are doing the other work.

  • And I think, then, what Bob said was that

  • we created a hub where other experts could plug into,

  • and I think, in any other issue area that you're working on,

  • you can't just talk about it and convene people.

  • You actually have to build, and when you build,

  • you create a platform that others can add to,

  • and I think that is one of the core reasons why

  • we have seen so much progress,

  • is we started out convening and really realized

  • that wasn't gonna last very long,

  • and then we built, and once we started building, we scaled.

  • >> So, you got in the market quickly with something.

  • >> Yeah.

  • >> So, one of the issues with

  • any sort of criminal enterprise

  • is it tends to end up in a bit of an arms race,

  • so you've built this great technology but then

  • you've gotta keep one step ahead of the bad guys.

  • So, how are you actually doing that?

  • How are you continuing to invest in this and develop it

  • to make sure that you're always one step ahead?

  • >> So, I can address that on a couple of levels.

  • One is, you know, working with Thorn,

  • and I lead a program at Intel called

  • the Safer Children Program, where we work with

  • Thorn and also the National Center

  • for Missing and Exploited Children.

  • Those conversations bring in all of the tech giants,

  • and there's a little bit of sibling rivalry.

  • We're all trying to throw in our best tech.

  • So, I think we all wanna do as well as we can

  • for these partnerships.

  • The other thing is, just in very tactical terms,

  • working with Thorn, we've actually,

  • Thorn and with Microsoft, we've created a capability

  • to crowdsource more data

  • to help improve the accuracy

  • of these deep learning algorithms.

  • So, by getting critical mass around this problem,

  • we've actually now created enough visibility

  • that we're getting more and more data.

  • And as you said earlier, it's a data problem,

  • so if you have enough data,

  • you can actually create the models with the accuracy

  • and the capability that you need.

  • So, it starts to feed on itself.

  • >> Julie talked about the business logic,

  • how she attacked that.

  • That's really, 'cause I think one thing notable,

  • good use case, but from a tech perspective,

  • how does the cloud fit in with Intel specifically?

  • Because it really, the cloud is an enabler too.

  • >> Bob: Yeah, absolutely.

  • >> How's that all working with Intel?

  • And you go on about whole new territory

  • you guys are forging in here, it's awesome, but the cloud.

  • >> Right, so, for us, the cloud is

  • an incredible way for us to make our compute capability

  • available to anyone who needs to do computing,

  • especially in this data-driven algorithm era where

  • more and more machine learning, more and more AI,

  • more and more data-driven problems

  • are coming to the fore,

  • doing that work on the cloud and being able to

  • scale your work according to how much data

  • is coming in at any time,

  • it makes the cloud a really natural place for us.

  • And of course, Intel's hardware is a

  • core component of pretty much all the cloud

  • that you could connect to.

  • >> And the compute that you guys provide,

  • and Amazon adds to it, their cloud is impressive.

  • Now, I'd like to know what you guys

  • are gonna be talking about in your session.

  • You have a session here at re:Invent.

  • What's the title of the session, what's the agenda,

  • is it the same stuff here, what's gonna be talked about?

  • >> So, we're talking about life-changing AI applications,

  • and in specific we're gonna talk about,

  • at the end Julie will talk about

  • what Thorn has done with the child-finder and the AI

  • that we and Microsoft built for them.

  • We'll also, I'll start out by talking about

  • Intel's role broadly in the computing and AI space.

  • Intel really looks to take all of its different hardware,

  • and networking, and memory assets,

  • and make it possible for anybody to do the kinds of

  • artificial intelligence or machine learning

  • they need to do.

  • And then in the middle, there's a really cool

  • deployment on AWS sandwich that (something)

  • will talk about how they've taken the models

  • and really dialed them up in terms of

  • how fast you can go through this data,

  • so that we can go through millions and millions of images

  • in our searches, and come back with results

  • really, really fast.

  • So, it's a great sort of three piece story about

  • the conception of AI, the deployment at scale

  • and with high performance,

  • and then how Thorn is really taking that