 from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and it's ecosystem partners. Welcome back to theCUBE's continuing coverage of our event, Big Data SD. I'm Lisa Martin, joined by Dave Vellante. And we've been here all day, having some great conversations, really looking at Big Data, cloud, AI machine learning from many different levels. We're happy to welcome back to theCUBE one of our distinguished alumni, Ron Bodkin, who's now the technical director of Applied AI at Google. Hey, Ron, welcome back. Oh, it's nice to be back, Lisa, thank you. Yeah, thanks for coming by. Thanks, Dave. So you have been a friend of theCUBE for a long time. You've been in this industry, in this space for a long time. Let's take a little bit of a walk down memory lane. Your perspectives on Big Data Hadoop and the evolution that you've seen. Sure, you know, so I first got involved in Big Data back in 2007. I was VP Engineering at a startup called Quantcast in the online advertising space. We were using early versions of Hadoop to crunch through petabytes of data and build data science models. And I saw a huge opportunity to bring those kind of capabilities to the enterprise. We were working with early Hadoop vendors. Actually, at the time, there was really only one commercial vendor of Hadoop. It was Cloudera, right? And we're working with them and then others as they came online. So back then we had to spend a lot of time explaining to enterprises, what was this concept of Big Data? Why was Hadoop an open source Big Data interesting? What did it mean to build a data lake? And, you know, we always said, look, there's going to be a ton of value around data science, right? Putting your Big Data together, collecting complete information and then being able to build data science models to act in your business. So, you know, the exciting thing for me is, you know, now we're at a stage where many companies have put those assets together. You've got access to amazing cloud scale resources like we have at Google to not only work with great information, but to start to really act on it. Because, you know, kind of in parallel with that evolution of Big Data was the evolution of the algorithms as well as, you know, the access to large amounts of digital data that's propelled, you know, a lot of innovation and AI through this new trend of deep learning that we're invested heavily in. I mean, the epiphany of Hadoop, when I first heard about it, was bringing, you know, five megabytes of code to a petabyte of data as sort of the bromide. But, you know, the narrative in the press has really been, well, they haven't really lived up to expectations. The ROI has been largely reduction on investment. And so, is that fair? I mean, we've worked with practitioners, you know, all your Big Data career and you've seen a lot of companies transform. Obviously Google's Big Data company is probably the best example of one. Do you think that's a fair narrative or did the Big Data hype fail to live up to expectations? You know, I think there's a couple of things going on here. One is, you know, that the capabilities in Big Data have varied widely, right? So if you look at the way, for example, at Google we operate, you know, with Big Data tools that we have, they're extremely productive, work at massive scale, you know, with large numbers of users being able to slice and dice and get deep analysis of data. It's a great setup for doing machine learning, right? That's why we have things like BigQuery available in the cloud. You know, I'd say that what happened in the open source Hadoop world was, it ended up settling in on more of the subset of use cases around how do we make it easy to store large amounts of data inexpensively? How do we offload ETL? How do we make it possible for data scientists to get access to raw data? I don't think that's as functional as what people really had imagined coming out of Big Data, but it sort of served a useful function in complimenting what companies were already doing at their warehouse, right? So I'd say those efforts to collect Big Data and to make them available have really been a, they've set the stage for analytic value both through better building of analytic databases, but especially through machine learning. And there's been some clear successes. I mean, one of them obviously is advertising. Google said a huge success there, but much more in fraud detection, you're starting to see, you know, healthcare really glomm on, financial services have been big on this. You know, maybe largely for marketing reasons, but also risk, you know, for sure. So there's been some clear successes. I've likened it to, you know, when you, before you got to paint, you got to scrape and you got to, you know, you put in caulking and so forth, and now we're in a position where you've got a corpus of data in your organization, and you can really start to apply things like machine learning and artificial intelligence. Your thoughts on that premise? You know, I mean, I definitely think there is a lot of truth to that. I think some of it was, there was a hope that a lot of people thought that, you know, Big Data would be magic, that you could just dump a bunch of raw data without any effort, and out would come, you know, all the answers, right? And, you know, that was never a realistic hope, right? There's always a level of, you have to at least have some mobile structure in the data. You have to put some effort in curating the data so you have valid results, right? So it's created a set of tools to allow scaling. You know, I mean, we now take for granted the ability to have elastic data, to have it scale and have it in the cloud in a way that, you know, that just wasn't the norm. Even 10 years ago is like, people were thinking about very brittle, limited amounts of data in silos was the norm. So the conversations changed so much, we almost forget how much things have evolved. Speaking of evolution, tell us a little bit more about your role with applied AI at Google. What was the genesis of it, and how are you working with customers for them to kind of leverage this next phase of Big Data and applying machine learning so that they really can identify, well, monetize content and data, and actually identify new revenue streams? Absolutely, Lisa. So, you know, at Google, we really started the journey to become an AI-first company early this decade. You know, a little over five years ago, we invested in the Google X team. Jeff Dean was one of the leaders there, started to invest in, hey, these deep learning algorithms are having a big impact, right? And, you know, Fei-Fei Li, who's now the chief scientist at Google Cloud, was at Stanford doing research around how can we teach a computer to see and catalog a lot of digital data for visual purposes, right? So combining that with advances in computing, with first GPUs, and then ultimately we invested in specialized hardware that made it work well for us, and massive-scale TPUs, right? That combination really started to unlock all kinds of problems that we could solve with machine learning in a way that we couldn't before, right? So, it's now become central to all kinds of products at Google, whether it be the biggest improvements we've had in search and advertising, coming from these deep learning models, but also breakthroughs like, products like Google Photos, where you can now search and find photos based on keywords from intelligence in a machine that looks at what's in the photo, right? So, we've invested and made that a central part of the business, and so what we're seeing is, as we build out the cloud business, there is a tremendous interest in how can we take Google's capabilities, right? Our investments in open source, deep learning frameworks, TensorFlow, right? Our investments in hardware, TPU, our scalable infrastructure for doing machine learning, right? Where we're able to serve, you know, billion inferences a second, right? So, we've got this massive capability we've built for our own products that we're now making available for customers. And customers are saying, how do I tap into that, right? How can I work with Google? How can I work with the product? How can I work with the capabilities? And so, you know, the Applied AI team is really about how do we help customers drive these 10x opportunities with machine learning, partnering with Google. And the reason it's a 10x opportunity is, you've had a big set of improvements where models that weren't useful commercially until recently are now useful and can be applied, right? So you can do things like translating languages automatically, like recognizing speech, like having automated dialogue for chatbots, or, you know, all kinds of visual APIs, like our AutoML API, where engineers can feed up images and it will train a model specialized to their need to recognize what you're looking for, right? So those types of advances mean that all kinds of business processes can be reconceived of and dramatically improved with automation taking a lot of human drudgery out, right? So customers are like, that's really exciting. And at Google, you're doing that. How do we get that, right? We don't know how to go there. Well, natural language processing has been amazing in the last couple of years. Not surprising that Google is so successful there. I was kind of blown away that Amazon with Alexa sort of blew past Siri, right? And so thinking about new ways in which we're going to interact with our devices, it's clearly coming. So it leads me to my question on innovation. What's driven, in your view, innovation in the last decade and what's going to drive innovation next 10 years? I mean, I think innovation is very much a function of having the right kind of culture and mindset, right? So, I mean, for us at Google, a big part of it is what we call 10X thinking, right? Which is really focusing on, how do you think about the big problem and work on something that could have a big impact? You know, I also think that you can't really predict what's going to work, but there's got a lot of interesting ideas and many of them won't pan out, right? But the more you have a culture of failing fast and trying things and at least being open to the data and give it a shot, right? And say, is this crazy thing going to work, right? I mean, that's why we have things like Google X where we invest in moon shots, but that's why throughout the business, we say, hey, you can have a 20% project, you can go work on something and many of them don't work or have a small impact, but then you get things like Gmail getting created out of a 20% project, right? It's a cultural thing that you foster and encourage people to try things and be open to the possibility that something big is on your hands, right? On the cultural front, it sounds like in some cases, depending on the enterprise, it's a shift in some cases, it's a cultural journey. The Google on Google story sounds like it could be a blueprint. How do we do this? You've done this, but how much is it a blueprint on the technology capitalizing on deep learning capabilities as well as a blueprint for helping organizations on this cultural journey to be actually being able to benefit and profit from this? Yeah, I mean, it's absolutely right, Lisa, that these are both really important aspects, right? That there's a big part of the cultural journey. You know, in order to be an AI first company to really reconceive your business around what can happen with machine learning, it's important to be a digital company, right? To have a mindset of making quick decisions and thinking about how data impacts your business, right? And activating in real time, right? So there's a cultural journey that companies are going through, right? How do we enable our knowledge workers to do this kind of work? How do we think about our products in a new way? How do we reconceive, think about automation, right? There's a lot of these aspects that are cultural as well, but I think a big part of it is, you know, it's easy to get overwhelmed, right, for companies, but it's like, you have to pick somewhere, right? What's something you can do? What's a true north? What's an area where you can start to invest and get impact and start the journey, right? Start to do pilots, start to get something going. What we found, you know, certainly I found in my career has been when companies get started with the right first project and get some success, they can build on that success and invest more, right? Whereas, you know, if you're not experimenting and trying things and moving, you're not, you're never going to get there. Momentum is key. Well, Ron, thank you so much for taking some time to stop by theCUBE. I wish we had more time to chat, but we appreciate your time. Oh, it's great to be here again. See you again, thanks. We want to thank you for watching theCUBE Live from our event, Big Data SV in San Jose. I'm Lisa Martin with Dave Vellante. Stick around, we'll be back with our wrap shortly.