>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Hey welcome back, everyone, it's theCUBE's coverage here live in Las Vegas for re:Invent 2019, this is theCUBE's seventh year covering re:Invent, the event's only been going for eight years, it feels like a decade, so much growth, so much action, I'm John Furrier with my co-host Dave Vellante, here extracting the signal from the noise in the Intel AWS studio of theCUBE, thank you for that sponsorship. Mike Miller is our next guest, he's director of AI devices at AWS, super excited for this segment, because DeepRacer's here, and we got some music, AI is the front and center, great to see you again, thanks for coming on. >> Absolutely, thank you for having me on again, I appreciate it. >> All right, let's just jump right in, the toys. Developers are geeking out over DeepRacer and the toys you guys are putting out there as a fun way to play and learn. >> Absolutely, getting hands-on with these new broadly applicable machine learning technologies. >> Let's jump into DeepRacer, so first of all, give us a quick update on what's happened between last year and this year in the DeepRacer community, there's been a lot of froth, competitiveness, street battles, and then we'll get an update, give us a quick update on the community. >> So we launched DeepRacer last year as a 1/18 scale race car designed to teach reinforcement learning, so this thing drives by itself around the tracks. We've got an online experience where customers can train models, so we launched a DeepRacer league where we plan to visit 22 sites around the world at AWS summits, where developers can come visit us and race a car physically around a track, and we had online contests, so every month we had a new track for developers to be challenged by and race their cars around the track. We've seen tremendous engagement and excitement, a little bit of competition really gets developers' juices going. >> It's been a lot of fun, congratulations, by the way. >> Absolutely, thank you. >> All right, let's get into the new toy, so DeepRacer 2.0, whatever you're calling it, just DeepRacer-- >> DeepRacer Evo. >> Evo, okay. >> New generation, so we've basically provided more opportunities to race for developers, more challenges for them to learn, and more ways for them to win. So we integrated some new sensors on this car, so on top there's a LIDAR, which is a laser range finding device that can detect other cars or obstacles in the rear of the car and to the sides, and in the front of the car we have stereo cameras that we added so that the car can sense depth in front of it, so with those new sensors, developers can now be challenged by integrating depth sensing and object avoidance and head to head racing into their machine learning models. >> So currently it's not an obstacle course, correct, it's a race track, right? >> So we call it a time trial, so it's a single car on the track at a time, how fast can you make a lap, our world record actually is 7.44 seconds, set by a young lady from Tokyo this past year, really exciting. >> And she was holding up the trophy and said this is basically a dream come true. And so, what are they trying to optimize, is it just the speed at the turn, what are they sort of focused on? >> Yeah, it's a little bit of art and a little bit of science, so there's the reinforcement learning model that learns through what's called a reward function, so you give the car rewards for achieving specific objectives, or certain behaviors, and so it's really up to the developer to decide what kind of behaviors do they want to reward the car with, whether it's stay close to the center line, reduce the amount of turns, they can also determine its position on the track and so they can reward it for cutting corners close, speeding up or slowing down, so it's really a little bit of art and science through some experimentation and deciding. >> So we had Intel on yesterday, talking about some of their AI, Naveen Rao, great guy, but they were introducing this concept called GANs, Generative Adversarial Networks, which is kind of like neural network technology, lot of computer science in some of the tech here, this is not kiddie scripting kind of thing, this is like real deal. >> Yeah, so GANs actually formed the basis of the product that we just announced this year called DeepComposer, so DeepComposer is a keyboard and a cloud service designed to work together to teach developers about generative AI, and GANs are the technique that we teach developers. So what's interesting about generative AI is that machine learning moves from a predictions-based technology to something that can actually create new content, so create new music, new stories, new art, but also companies are using generative AI to do more practical things like take a sketch and turn it into a 3D model, or autocorrect colorize black and white photos, Autodesk even has a generative design product, where you can give, an industrial designer can give a product some constraints and it'll generate hundreds of ideas for the design. >> Now this is interesting to me, because I think this takes it to, I call basic machine learning, to really some more advanced practical examples, which is super exciting for people learning AI and machine learning. Can you talk about the composer and how it works, because pretend I'm just a musician, I'm 16 years old, I'm composing music, I got a keyboard, how can I get involved, what would be a path, do I buy a composer device, do I link it to Ableton Live, and these tools that are out there, there's a variety of different techniques, can you take us through the use case? >> Yeah, so really our target customer for this is an aspiring machine learning developer, maybe not necessarily a musician. So any developer, whether they have musical experience or machine learning background, can use the DeepComposer system to learn about the generative AI techniques. So GANs are comprised of these two networks that have to be trained in coordination, and what we do with DeepComposer is we walk users through or walk developers through exactly how to set up that structure, how these two things train, and how is it different from traditional machine learning where you've got a large data set, and you're training a single model to make a prediction. How do these multiple networks actually work against each other, and how do you make sure that they're generating new content that's actually of the right type of quality that you want, and so that's really the essence of the Generative Adversarial Networks and these two networks that work against each other. >> So a young musician who happens to like machine learning. >> So if I give this to my kid, he'll get hooked on machine learning? That's good for the college apps. >> Plug in his Looper and set two systems working together or against each other. >> When we start getting to visualization, that's going to be very interesting when you start getting the data at the fundamental level, now this is early days. Some would say day zero, because this is really early. How do you explain that to developers, and people you're trying to get attention to, because this is certainly exciting stuff, it's fun, playful, but it's got some nerd action in it, it's got some tech, what are some of the conversations you're having with folks when they say "Hey, how do I get involved, why should I get involved," and what's really going to be the impact, what's the result of all this? >> Yeah, well it's fascinating because through Amazon's 20 years of artificial intelligence investments, we've learned a lot, and we've got thousands of engineers working on artificial intelligence and machine learning, and what we want to do is try to take a lot of that knowledge and the experiences that those folks have learned through these years, and figure out how we can bring them to developers of all skill levels, so developers who don't know machine learning, through developers who might be data scientists and have some experience, we want to build tools that are engaging and tactile and actually tangible for them to learn and see the results of what machine learning can do, so in the DeepComposer case it's how do these generative networks actually create net new content, in this case music. For DeepRacer, how does reinforcement learning actually translate from a simulated environment to the real world, and how might that be applicable for, let's say, robotics applications? So it's really about reducing the learning curve and making it easy for developers to get started. >> But there is a bridge to real world applications in all this, it's a machine learning linchpin. >> Absolutely, and you can just look at all of the innovations that are being done from Amazon and from our customers, whether they're based on improving product recommendations, forecasting, streamlining supply chains, generating training data, all of these things are really practical applications. >> So what's happening at the device, and what's happening in the cloud, can you help us understand that? >> Sure, so in DeepComposer, the device is really just a way to input a signal, and in this case it's a MIDI signal, so MIDI is a digital audio format that allows machines to kind of understand music. So the keyboard allows you to input MIDI into the generative network, and then in the cloud, we've got the generative network takes that input, processes it, and then generates four-part accompaniments for the input that you provide, so say you play a little melody on the keyboard, we're going to generate a drum track, a guitar track, a keyboard track, maybe a synthesizer track, and let you play those back to hear how your input inspired the generation of this music. >> So GANs is a big deal with this. >> Absolutely, it forms the basis of the first technique that we're teaching using DeepComposer. >> All right, so I got to ask you the question that's on everyone's mind, including mine, what are some of the wackiest and/or coolest things you've seen this year with DeepComposer and DeepRacer because I can imagine developers' creativity straying off the reservation a little bit, any cool and wacky things you've seen? >> Well we've got some great stories of competitors in the DeepRacer league, so we've got father-son teams that come in and race at the New York summit, a 10 year old learning how to code with his dad. We had one competitor in the US was at our Santa Clara summit, tried again at our Atlanta summit, and then at the Chicago summit finally won a position to come back to re:Invent and race. Last year, we did the race here at re:Invent, and the winning time, the lap time, a single lap was 51 seconds, the current world record is 7.44 seconds and it's been just insane how these developers have been able to really optimize and generate models that drive this thing at incredible speeds around the track. >> I'm sure you've seen the movie Ford v Ferrari yet. You got to see that movie, because this DeepRacer, you're going to have to need a stadium soon, with eSports booming, this has got its own legs for its own business. >> Well we've got six tracks set up down at the MGM Grand Arena, so we've already got the arena set up, and that's where we're doing all the knock-out rounds and competitors. >> And you mentioned father-son, you remember when we were kids, Cub Scouts, I think it was, or Boy Scouts, whatever it was, you had the pinewood derby, right, you'd make a car and file down the nails that you use for the axles and, taking it to a whole new level here. >> It's a modern-day version. >> All right, Mike, thanks for coming on, appreciate it, let's keep in touch. If you can get us some of that B-roll for any video, I'd love to get some B-roll of some DeepRacer photos, send 'em our way, super excited, love what you're doing, I think this is a great way to make it fun, instructive, and certainly very relevant. >> Absolutely, that's what we're after. Thank you for having me. >> All right, theCUBE's coverage here, here in Las Vegas for our seventh, Amazon's eighth re:Invent, we're documenting history as the ecosystem evolves, as the industry wave is coming, IoT edge, lot of cool things happening, we're bringing it to you, we're back with more coverage after this short break. (techno music)