>> Announcer: Live from Las Vegas. Its theCube, covering NAB 2017. Brought to you by HGST. >> Hi welcome back to theCube, we are live from NAB 2017. I'm Lisa Martin in Las Vegas, excited to be joined by the co-founder of Avalanche, Josh Kolden. Hey Josh, welcome to theCube. >> Thank you. >> So tells us a little bit about what Avalanche is. >> Well, Avalanche is a file navigator for film makers. It allows, the difference being from something like Windows Explorer or an Apple finder, is that it allows you to work with files wherever they are, on different computers, in the cloud, on different units of production as they're moving around the world. Without having to do all the low-level coordinating of that data. >> So in media we're talking about massive files. How is this different from Dropbox, Box, et cetera? >> So those tools actually try to synchronize your data. So they, if you put a big media file in Dropbox it'll try to copy not only the file to the cloud but also of course to any other computers you have your Dropbox running on. What Avalanche is doing, doesn't necessarily can move it, but it doesn't necessarily move it. Instead, let's say you're an editor or studio and you want to see what's happening on set, you can see all the files as they're coming off of a camera and interact with them. Rename them, make notes, whatever has to happen, see the notes that are already applied to them. And when those files show up in editorial, in say hard drive that's when all that happens, and gets synchronized locally. So it allows people to work in a very intuitive and natural production workflow, without actually trying to copy huge amounts of data across the net. >> In terms of like the production life cycle, are we talking about pre-production, production, post-production, or the whole kit and caboodle? >> It's the whole thing, because what happens in production is you see teams of people kind of ad hoc join the production, they might have teams during pre-production that are there for a bit and teams that come on in post-production. So there's always this coordination problem of knowing who has what, you know, where is the camera? Post-production's looking for camera imports that only people that were on set know about. And this provides a mechanism to kind of have a continuity between all those different teams across the entire production pipeline. >> Continuity is key. What, give us an example, you had mentioned, and this is really built for filmmakers. If something is filmed and the crew or the director decides, you know what, that would've been great if we'd actually shot that for VR. What's the process of them, or how simple is it or seamless for them to go back in, pull something out, change it? >> Well, in those kinds of situations, I mean production generally, usually has a lot of planning involved. So you're going to know going in those kinds of issues, if it's something as big as, we want to have extra footage for VR or whatever. But one thing that happens is, let's say for example, there's a costume change where you've got a product which is a suit or something, that needs to be placed in the scene for the financing and then somebody spills something on it, but story-wise that works, so they're going to keep it in. People that are in the product teams later down the line might need to know these changes have occurred so they can either pushback and say, no we need to re-shoot that with a clean suit, or whatever that information might be. That back and forth. So this makes that even possible at all. Before it would just be making sure that somebody on production called the, that team and explained it to them. Right now, with this, you can just put a quick note on any device and it eventually be findable, you can just search it like Google, and find any information related to that suit, or that shot, or that production day. Any kind of different ways of searching for the stuff you're looking for. >> So facilitating a little bit of automation. You talk about the connectivity, but also it sounds like the visibility is there, much more holistic. >> Yeah we call it discoverability, because right now a lot of the stuff isn't discoverable. Once, say you don't know what row database entry is, once you've lost that row number. There's no way to find out where that data comes from anymore, it's just completely disconnected. So we use a framework, it's open sourced underneath, called C4, the Cinema Content Creation Cloud and that framework provides a mechanism that what they called indelible metadata where it binds attributes to media in a way that doesn't easily get lost. So downstream you can discover relationships you didn't expect to be there. You don't have to preplan all the relationships and build them in advance. >> So one of the things you and I were chatting about before we went live is how, how Silicon Valley approaches this cloud. Versus how Hollywood approaches it. Tell us a little bit more about your insights there, I thought it was very intriguing. >> Yeah, this is a really interesting thing because not a lot of people realize, because a lot of people were on both sides, Hollywood and Silicon Valley, were using the same terminology. We're talking about the cloud, we're talking about files, we're talking about copying things. But there's subtle differences that get lost. And so what I've been working on a lot in the open sourced community, and in standards is helping to communicate this new concept that what we really need is, like a web for media production. With a normal web that most of Silicon Valley and cloud tools are built on, you're expecting to be able to transfer all your data each time. You go to the website, you get the webpage right then, you get all the images that it links to right then. But you don't want to do that when you're doing media production cause that might represent terabytes of data for each shot. And you need to work relatively quickly. You might be doing renders or composites, these things might take many many many elements to layer together. You can't be requesting this data as you need it every single time. You want to kind of get there and use, do all the processing you can possibly do all at once. So an architecture like that calls for a different kind of internet. An internet where your data moves less often. You get it to the cloud and you leave it there, and you do all your processing on it. Or it's in editorial, you do all your editing with it. The pieces that you need are in the right places, and you move them as little as possible. You move, command and control and metadata between those locations, but the media itself needs to arrive either maybe by hard drive or get synced in advance, there's different ways of that moving, but it doesn't happen at the same time that the command and control is happening. So yeah, we are trying to communicate that difference. That Hollywood is used to it happening because they have the data center in their building. Silicon Valley's used to it happening because it's small data across the network. And that's where that disconnect is happening, is they both think it's just a quick call, but it works for them because of a different architecture that they're building on top of. >> Different architectures, different, I imagine objectives. How are you helping to influence Silicon Valley coming together with Hollywood and really them influencing each other? Whether it's Hollywood influencing the type of internet that's needed and why, and Silicon Valley influencing maybe get away from the on prem data centers. Leverage hybrid as a destination, as a journey. Leverage the cloud for economies of scale. What's that influence like? >> Yeah, it's really fantastic because I think it's a perfect, it's really really good relationships between the kinds of skill-sets that Silicon Valley companies bring to the table, and the kinds of creations talent that Hollywood has. In fact, there's a lot of what Hollywood production studios don't want to have to invest in. They don't want to have a data center. If they can have a secure, productive, as you need it tool set, that they turn up they performance on when they're in production and then turn it off when they're done. That's exactly what we do with camera equipment. We rent it for the production and we give it back. So we're used to in Hollywood, that production model. So it's kind of teed up and ready to use all those services, it's just this kind of plumbing level that has been everybody's pain point. >> So from a collaboration perspective, are you facilitating, like a big cloud provider meeting with one of the big studios and really collaborating to kind of cross pollinate? >> Yeah so, I've been working with the Entertainment Technology Center, that's funded, at USC yeah, they're funded by all the major studios, and have other members like Google and other big vendors for cloud and whatnot. And these groups are very interested in trying to collaborate with technology companies and figure out the best ways to work together. And I have a lot of experience with cloud and computer technology and Silicon Valley style services. And also for production. So I've been working extensively in trying to bridge that gap, in terms of the understanding, but also in terms of some fundamental tools like I was saying, the open source framework, C4, so that, kind of like the web and HTML and all that stuff came about. Nobody could go to that level of the internet and create that new economy of the internet until those foundations were in place. So that's what we've been pushing. >> Speaking of foundation, last question before we wrap here. Where are you in this, kind of first use case example of the meeting of the minds? How close are you to really fixing this facilitated to really support what both sides need? >> We've actually been doing a number of production tasks over at ETC. We've shot several short films using these things. So all these things are actually in place and usable today. It's just a matter of getting people to start using them, be aware of them. They're all free and, you know, easy to use, relatively for technical people, for Silicon Valley people. And then there's going to be another layer that we're really, that's why we're talking a lot about it, that's going to be the software companies and the hardware companies supporting it. We're pushing it through standards. So it'll be showing up on everybody's radar soon. And we'll see higher level integrations, so the digital artists that don't know how to do that lower level software stuff will just get it for free from the tools they use. And that's kind of what the Avalanche file manager does, it provides a lot of that cloud technology underneath and you don't have to worry about it, it just looks like a file manager. >> Very exciting. Thanks so much Josh for sharing your insights and what you're working on. We look forward to seeing those things coming to the forefront very soon. >> Alright, thank you. >> Thanks for joining us on theCube and we want to thank you for watching theCube. Again I'm Lisa Martin, we are live at NAB 2017, in Las Vegas, but stick around we will be right back.

>> Narrator: Live from Las Veags, it's theCube, Covering NAB 2017. Brought to you by HGST. >> Welcome back everybody, Jeff Frick here with theCube. We're at NAB 2017 with 100,000 of our closest friends. Talking all about media, entertainment and technology. The theme this year is MET, cause the technology is so mixed in with everything else that you can't separate it anymore. And we're really excited to do a deep dive into kind of the customer, or not the customer, excuse me, the consumer side of this whole world with Yves Bergquist. He's the project director, Data and Analytics Entertainment Technology Center at USC. So Yves welcome. >> Thank you, thanks for having me. >> So when I was doing some research on your segment, really interesting to see that you're involved very much in trying to figure out what people like to watch how they like to watch and get a bunch of data because now the choices for the consumers of media and entertainment are giant, like never before. >> Yeah. There's a, very very basic question that I think not a lot of people in media and entertainment can answer. Is that why are people watching your stuff? And they have sort of surface level answers, but there's ways that the content out there, that we watch, resonates cognitively with us, that is really important, is very fundamental in how we consume media and entertainment. And even the decision making of why we decide to go watch a show on Netflix, or play a mobile game, or watch a Youtube video. Why do we make these specific choices? What drives those choices? All these questions don't have a lot of really good answers right now, and that's where I, where we're focus all of our work at ETC. Is to really understand people's drive to entertain themselves or decisions to entertain themselves at a very deep level. And really understand how various narrative structures in film and trailers and brands and advertising resonate with people at a cognitive level. >> So it's pretty intersting, it really goes with the whole big data theme and the AI theme. Because now you can capture, collect, measure data in ways, and consumption in ways you couldn't ever do before. >> Yeah, that's a good point. So, you know, there's three things that are really impacting the media and entertainment industry and every industry, really. It's, number one, the ability to think in systems, right? We used to think about problems in a very sort of siloed manner, right, we think about a problem in isolation with other forces. Like we look at the flu in isolation with the environment that we're in, so like that. There's another way to look at things, in a more holistically, it's a system called systems thinking. And the ability to think of audiences as a system, just like your body's a system inside a system, right, is really revolutionizing the way we're looking at entertainment and media. The second thing thing is the availability of data, just there's an enormous amount of data out there. A lot of it is unstructured, but there's, the good thing about entertainment and media is that it drives passion and drives conversation. And anything that drives passion and conversations get very rich in data. And the third thing that is impacting the industry is machine living and AI. And the ability to really look at all of these data points across the system holistically in a very intelligent more semantic manner. And make sure that you're measuring the right things. For a very very long time the media and entertainment industry has been measuring the wrong things. And it's really now catching up very very fast and making sure that it's measuring the right things. For example, how do we measure how specific narrative structures in film resonate with people cognitively in a way that translates into the box office? Is there a specific character journey that resonates better in an action movie with males versus females. How does that matter for how a story's being told? Where do you innovate in script, right? Interesting point is the entertainment industry is very unique in that it has two major problems. Number one, its clients, its customers are absolute experts in the product. Because if you're 25 or 35, how many movies have you watched? Thousands of movies, right? So you're an expert in movies. >> Jeff: Certainly the ones you like. >> Exactly. If you're 25 you haven't bought hundreds or thousands of cars, right? So, but on the other hand the supplier of the content doesn't know as much of the customer as the customer knows about the product. So you have two problems. You have a really really really highly expert client, and, but you don't know a lot about that client as a studio, right, or a network or a media company. So that's very very unique distinct challenge that they're starting to get very very smart and very advanced in thinking about. >> The other thing is, that I see in the movie industry and I'm no expert by any stretch of the imagination but it seems like the compression pressure is huge. The budgets have grown to be giant. And the number of available weekends for your release are small. And the competition for attention and eyeballs around those weekends, it just seems to really have a really high kind of risk reward profile that's getting more and more extreme. And is that driving people more to kind of the known? Or is it just my perception that they're taking less risks on modifications from the script or modifications of kind of the norm especially around these big budget? I mean just the fact that you've got version 1,2,3,4,5,6, pick your favorite theme seems to be a trend that continues and gets even more, I mean Superman. How many Superman movies are there, or Spiderman? >> So you know, that's really interesting right? So the very natural tendency of the media and entertainment industry is when it doesn't know, as I was mentioning, it doesn't know as much as it could or should know about who its audience is. The tendency is then becomes to just take less and less risk in telling stories exactly the same way that's why you see a lot of really really formative very formulaic movies. What we're trying to do is, and the challenge with that is that, again you have an audience of experts and so if every single movie looks like the same one, look like the other one, you're going to have a problem. People aren't going to go see, going to go gravitate towards another kind of entertainment or some of your competitors. So you have to know where do you meet peoples expectations in a movie and where do you innovate? Deadpool is a really interesting example. Deadpool has the structure of a basic superhero movie but it has a lot of innovation underneath that. And so for the studios knowing where do you stick to the formula and where do you innovate in telling a story when you make a billion dollar movie, is going to become more and more interesting. Because if you innovate too much you're going to turn people off. If you don't innovate enough, you're going to turn people off. So we actually have some research looking at the mathematical definition of why we think certain things are interesting and certain things are not interesting so we can separate. These are the things you need in your movies, this is some aspects, if you go back to Deadpool, there's some aspects of Deadpool as a movie that are very traditional to the superhero genre. And a lot of other aspects that are very very innovative. So you have to innovate in certain areas and you have to no innovate areas. And that's a real challenge, and so that's why we're really applying our work to looking at narrative structure in storytelling at ETC is because that's where a lot of the revenue opportunities and the de-risking opportunities are. >> And it's interesting before we went live you were talking about thinking of storytelling and narrative as a little bit less art and a little bit more science in terms of of thinking at in terms of algorithms and algorithmically. Because there are patterns there, there is data there. So what does some of the data that you measure to get there? You mentioned earlier that in the past people were measuring the wrong thing. What are the right things to measure? What are some of the things you guys are measuring now? >> Yeah, so you know, it is still very much an art, right? It's making it, making art a little bit more optimal, and optimizing art is what we're doing, but it's, it will remain art for a very long time. I think for, and since we're at NAB, sort if in a broadcasting environment, I think a lot of the measurements and systems that have been in place for decades now are looking at demographics. And demographics, whether you're a male or female, Your age, your ethnicity, or your income, used to predict what you would watch. It doesn't do that anymore, and if you have kids, you know like me, you watch the same thing that they're watching, you're playing the same video games that they're playing. I think there's a new way to measure things more cognitively and semantically and neuroscience is starting to get into the issue of why do we think certain stories are more interesting or more appealing than others. Why do certain stories lead us to make actual decisions more than others? And so I think at a very very basic level you have to unpack this notion of why do people go see this movie? And it's a system, you know, that decision happens in a system where some of the system is demographics, demographics aren't going to go away they're still predictive to a certain extent. But it's also, you know, cast, it's also who has recommended this movie. And what are the systems of influence in driving certain people to see a movie? And all these things, and of course, what we're focusing on, which is storytelling and narrative structure and how that, sort of translates to making decisions to see this movie. A lot, you know, we're still in the infancy of measuring all of the system in a very scientific granular way, but we're making very very quick progress. And so even things like understanding the ecosystem of influence around why certain communities are influenced to go see certain movies by other communities and what happens there, right. So I'll give you an example, we did, we pulled months of data on Reddit about where supporters of Hillary Clinton and where supporters of Donald Trump would engage on that topic. Are they talking about that amongst each other or are they really going out there and trying to convince other people to vote for Trump or to vote for Hillary Clinton? And we saw some, two radically different patterns. So pattern number one, the Clinton people would mostly engage with each other on Reddit. So that's cool and that has very little value because you're not being an ambassador. On the other hand, the Trump people were engaging far outside of the Trump subReddit and trying to convince people to join the movement, to donate, to vote for Trump. So we think there's a model there that can be ported to the entertainment industry, where if your fans, if your fan base is mostly engaging with each other it has less value than if your fan base is really going out there and really trying to get other people excited about your movie. And why do certain people get excited and how do your fans, what argument do your fans use out there to convince others to go see your movie. All these things we're looking at, and it's brand new world now for media because of all of these data points. >> The systems conversation is so interesting because it's not only the system, but the individual. But it's like you said, it's all these systems of influence today. Look at the Yahoo reviews, the Rotten Tomato reviews, you know, what are there, Reddit, you know, as a system of influence, who would have ever thought? >> Yeah and we're getting it, we're going into a world very quickly, we're going to be able to understand entertainment and storytelling and narrative and it's cognitive power almost on a neural network base. In looking at what kind of neural network in our brains get fired when we are exposed to this type of character, or this type of storyline, or this type of narrative mechanics. And so this is a really exciting time. >> The other thing that's interesting, we talked again a little bit before we turned the cameras on, is about the trailers. Because that's kind of the story within the story. And depending on your objectives, and the budget, you know, they can make all kinds of number of trailers, in very different way, to approach or to target very specific audiences. I wonder if you can get into that a little bit. >> Yeah so, you know in the media and entertainment industry decisions have been made, and if you think about it it's amazing that the media and entertainment industry has made so much money, so I think it's a testament of the enormous creative talent that's involved. But, you know, especially for trailers a lot of the decisions about trailers are made sort of looking what's worked in the past in a very sort of haphazard way. There really isn't a lot of data and analytics and science applied to, hey what kind of trailer, what structure of trailer do we need to put out there in each channel for each target audience to get them really excited about the movie? Because there's many different ways you can present a movie, right, and we've seen, we've all seen many different types of trailers for many different types of movies. What we're doing, and nobody's really worried about hey let's analyze, for example, the pace, right, the edit cuts, the structure of the edits for the trailer and how that resonates with people. And now we have the ability to do that because people, you know, we will count views on YouTube for example, or there will be a way to measure how popular a trailer is. So what we're doing is we're just measuring everything that we can measure about a trailer. Is it a complete story? What is the percentage of the trailer is the main character in? What is the percentage of the trailer that the influence character is in? We're looking at cast. Does a trailer with Ben Affleck, you know, work better if Ben Affleck is a lot in the trailer, or not a lot in the trailer? And what kind of trailer types work better for specific genres, specific target audience, specific channels? So we're really unpacking that into a nice little spreadsheet. And measuring all the things that we can measure. And the thing about this is, if you think about the amount of money that's involved in making these decisions, you know if you're a studio and you're spending 3,4,5 billion dollars a year in marketing expense, and my work can make it even 10 percent more efficient, that's like half a billion dollars in savings. >> That's a real number. >> That's enormous right? So it's a really exciting time for media and entertainment because there are all these things on the horizon to help them make better decisions, more data driven decisions. And really free up creators, because if we can tell the people who tell the stories in film every, you can innovate so much more now because we've, we know that we've boiled it down to a science, and we know that in this, if you have these four or five things in your script, everywhere else you can innovate, go nuts. I think it's going to free up a lot of creative talent. We're going to see a lot more interesting movies out there. >> The other piece I think, I mean obviously a trailer for a movie's one thing, but take that little genre of creative that's purely built to drive behavior and that's a commercial. And I always joke with my kids, I watch a lot of sports, and there'll be a car ad and I'm like, just think if you're the poor guy that gets the assignment to make another car ad, I mean, how many car ads have been made, and you've got to think creatively. But the data that you're talking about, in terms of the narrative, what types of shots, the cutting, based on the demographic that you're trying to go after for that specific ad. That must be tremendously valuable information. >> Yeah it is really valuable. So you know, our philosophy is that everything is story. You're tie is a story, your haircut's a story, you're cereal's a story, your cars, everything. We make decisions based on the narratives that other other people tell us and that we tell ourselves about how to represent the world. Simply because the universe out there and the reality out there is too complex for our brains to really represent as it is, so we have to simplify, compress it into a set of a behavioral script that says, okay I'm, it's sort of an executive summary of their reality. And though that executive summary is a story. And so it's especially powerful in driving how what we buy and how we consume things. And so, I've build a platform that looks at, that extracts very very structured data from conversations about what is the narrative structure about a specific brand. You know, is it focused more on, you know,emotions? Is it focused more on ethics? Is it focused more on the, sort of the utility of the product? And trying to correlate that to look at what kind of narrative structure's around your brand? What kind of story around your brand, drives more sales? And so that's really really interesting, in sort of understanding, again, that cognitive relationship between stories and how efficient they are in driving specific behavior. That is exactly what my research is about. >> Yves, we could go on all day, but unfortunately we are out of time. So thank you for spending a few minutes and dropping by. Fascinating conversation. Alright, he's Yves Bergquist from USC, where all the film stuff's happening. I'm Jeff Frick, you're watching theCube. We'll be back NAB 2017 after this short break. Thanks for watching. (uptempo rock music)