 Live, from Las Vegas, it's theCUBE. Covering IBM Think, 2018, brought to you by IBM. We're back at IBM Think 2018. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante, and this is day two of our wall-to-wall coverage of IBM Think. We've been doing IBM shows for years. This is the big, consolidated show. 30,000 to 40,000 people. Too many people to count. Cameron Clayton is here. He's a GM of Watson Content and IoT Platform at IBM. Thanks for coming on. Thank you very much for having me. So quite a show, right? A live show. Standing room only, and lots of great announcements. So tell us about your announcements. Yeah, so we got a couple of things we're really, really excited about. The team's been working really hard on for the last few months. One is a way to train Watson to make Watson even smarter than it already is out of the box. And so we've been building data kits by vertical industry. So for financial services, for travel and transportation, for the hospitality industry, for healthcare, and for government on how do you give Watson a high machine IQ right out of the gate, as opposed to having to train it in your area of industry. And so once again, we're really focused on making Watson the AI system for enterprise. And this is another step on that journey to make Watson really, really smart. So really productizing it in a way that's much easier to consume? Much easier to consume. And if you think about it, there's a lot of jargon in each industry, right? To be an expert in the industry, you're gonna know a lot of jargon, understand the context of that. And AI system doesn't know that unless it's taught that. And so we're teaching Watson that and then how to apply it successfully in each of those industries. So it's a pretty material leap forward in how we're training Watson. So it hits the content component. It hits the content. And in what industries you're knocking down, where are you starting? Yeah, so we're starting with financial services. We're launching in travel and transportation and in hospitality. So we're basically, this is a pretty fun one. I love food, but basically Watson went out and scanned the entire internet and collected all the recipes that it could find on the internet and trained itself on food. And so you can ask it now questions about food, what restaurants, but really specific things. If you're a vegan, you can find out what's available near you. If you're gluten intolerant, you can find out things on the menu like that. But then there's other things, like in the travel and transportation industry, virtual agents for travel agents, they can ask questions of Watson and it can ask very specific, very deep things, very much like a human would. And so you can say, a simple thing like, where should I stay in New York? And a human would respond, well, are you a member of any hotel rewards program? Normal chatbot wouldn't. It would just say, these are the list of the 4,000 hotels in New York. Watson will actually ask human-like questions to give you the best answer possible. But all that requires training and that's what we're built in with these Watson content data kits and we're really excited about it. So I'll come back to that. But so if I take that example of Watson Chef, there's this discussion on AI for the enterprise versus AI for consumers. Are you crossing over? I mean, it sounds that that was kind of a consumer-y application, but yeah, that's just an example. No, it's very much about AI for the enterprise, right? And so the four priority industries that we're focused on, first is financial services, the sweet spot for IBM. The second is supporting our government clients to make sure that Watson is trained in the language and the nuances of government. The third is Watson Health, so the healthcare industry, the regulation and the language itself, so everything from pharmacology, et cetera. And then the fourth is travel and transportation. So it's very much about making Watson the smartest AI system for enterprise. That's absolutely its focus. What's the IoT angle in your title? Yeah, so I run the IoT platform for IBM and so the weather company, which is how I joined IBM, which I also run really is one of the largest IoT platforms in the world, which was actually a big part of the acquisition case for acquiring the weather company. We're now bringing the ability to ingest 35 to 40 billion data requests every day with the weather company platform to the IoT platform where we can combine those things together so we can ingest data and content at a scale unlike pretty much anyone else in the world. Sort of second only to Google in terms of the scale of data and content we can ingest. And we use that data to help train Watson, on one hand, and on the other hand to support our clients in multiple industries around the world. Yeah, I remember when IBM did that acquisition, Bob Picciano told me, well, you got to understand this is an IoT play as much as it is a data science play. So how has that evolved? When you come together with IBM's core? Yeah, so I think in a couple of ways. One is it's taken the weather company was mostly a domestic US business. IBM in the last couple of years has globalized that business in a very material way. So a great example is in aviation where we have the top 30 US operators. Now we have hundreds of operators all around the world helping them make decisions every day. At its core, this IoT platform that started with the weather company is now much larger than that has grown into a decision platform. We make recommendations for people to make decisions. Mostly that's with Watson and AI, but sometimes it's just with machine learning and more traditional methods. So you got some other stuff going on. We were talking off camera about this real-time closed captioning. I was showing you our video clipper tool. You said, hey, we have something very similar. We're going to maybe talk and see if we can collaborate. I can't wait to try that out. So talk more about what you're doing with real-time closed captioning. It's a mandate for broadcasters and other folks like YouTube. How are you helping them? Yeah, so as you mentioned, closed captioning is a regulated space for broadcasters, both local and national. It's a cost center for them. They have to do it. And it takes time, people, effort, and energy. We're automating that and we're doing it in a real-time way, so in true real-time. So as we're speaking, Watson is listening, it's recording, and it's annotating everything that goes on in the video clip. It then is also breaking it up into essentially a highlight reel, right? And so you can ask questions, hey, show me the highlights of the US Open or the Masters Golf Tournament, and it will automatically select the very best clips that came from that tournament based on sentiment analysis, tone of voice, trending keywords that were showing in social media, and surface those clips up, typically to a human editor who will then process them. But it basically automates a system that today requires human intervention to deliver and makes it completely seamless by being real-time. So Watson will analyze social data, Twitter data, look at the, take the fire hose, and say, okay, based on the Olympics or whatever it was, this is what was hot. That's right. Curling was off the charts, hot. Curling was always hot. Hashtag curling. Right. Okay, cool. That's right. And this is a product that's out in the market today? It's part of this launching here, I think, and is being tested by multiple clients right now, and has got really great accuracy, quality scores, 95% plus accuracy. But most importantly, it's no human intervention. So no person has to do anything and it meets all of the regulatory requirements. For digital content creators, which are the fastest growing part of the video ecosystem, people like yourself and others, are also using it to automatically metatag all their clips. So not only does it do sentiment analysis of the clips and the content itself using the closed captioning, but it's also going out and measuring social media keywords and hashtags that are trending and looking for those keywords in the closed captioning and clipping that out and surfacing it to make it easier. And I consume that as a monthly service kind of thing. Exactly, exactly. How about GDPR? That's hard topic these days. Can you help me with my GDPR problem? There's my clock ticking on my- Clots ticking on GDPR. You haven't started on GDPR yet? You're in some trouble? You're way late. You're way late, but you better call IBM pretty quickly and we'll parachute in and try and help. How can you help? So I think we can help in multiple ways. So one is obviously our services group with GBS. We're doing thousands of engagements, trying to help people with GDPR. I think secondly is we've got a big effort with our consumer weather business to be ready for GDPR. We have 250 million users of our weather app around the world and they all have to be compliant here pretty quickly. And so we've got that all set up ready to go. And then these data kits also learn the regulations. So you can ask questions of Watson about GDPR and your specific use cases as a customer and it will show you how to apply the regulations of GDPR to your business. So early on you talked about these data kits. I mean, my head I was thinking SDK. So how does that all work? Yeah, so you can, you basically on a SaaS basis, you essentially rent these data kits, everything from like a general knowledge kit to an industry specific kit for financial services to a sub-industry like wealth management within financial services. And you basically can rent each of those pieces. Within the government category, we have a GDPR capability along with other regulatory capabilities within the data kits. Okay, so how does that work? I sort of train my internal systems. It's super easy. You basically go to Bluemix and you can just use it as a subscription out of Bluemix is the fastest, easiest way to do it. Secondly, you can talk to any of your IBM associates and about how to use data kits with Watson. It's always used in conjunction with Watson services themselves is how you basically deploy it in practice. So I've got, let's say I got data all over the place in my organization. It's siloed out and I'm freaking out because I got the personal data on an individual here and then one over here and one over here. What do I do? I point my corpus of data at Watson and it helps me to extract from entities, dedu, service. The first step in all of our engagements is to listen and understand exactly where all the data is and everyone's on a journey, right? From on-prem to a hybrid to some public cloud and everything in between. They don't know where they're looking at. And they don't know where it all is. And so, step one is for us to go in and listen. We try to, we have a rule in our group like two ears and one mouth, use them proportionally. And so we go in and we try to listen, find out, map out sort of a architecture of where our client's data is and then understand what problem they're really trying to solve because oftentimes there's lots of good ideas but there's only a couple of problems that really matter to that client to solve. Right now, GDPR is certainly one of those problems. But with its revenue or efficiency, we can help but we really need to understand what the problem set is first. And so we have a engineering team that goes in and does sort of architectural work and listens up front. And then we go into a sort of solutioning mode to solve problems. One of the questions we often ask on theCUBE is how far can we take machine intelligence? How far should we take machine intelligence? What are the things that machines can do that humans can't? How is that changing? How will they complement each other? How will they compete? You must think about that a lot in your role. You're augmenting, sometimes replacing a lot of human tasks. But what are your thoughts on those big picture questions? I think as a company, we're really, really hard to make sure that we are always augmenting people wherever possible. We fundamentally believe that every job is going to be changed by AI. But we believe that humans are really good at creativity, at curiosity, and at risk management. We don't really think about us being good at risk management, but from when we're born, just learning to walk is a risk management exercise. Look at any toddler wobbling learning to walk. You realize it's a risk management exercise. AI systems have to learn all these things. And so, surfacing and recommending decisions is what we believe Watson and AI is best equipped to do and then have a person actually make the final call. Great. All right, Cameron, thanks very much for coming to theCUBE. It's a pleasure meeting you. Absolutely. Look forward to the follow up. Absolutely. We'll follow up. All right, keep it right there, everybody. We'll be back with our next guest. Right after this short break, you're watching theCUBE live from IBM Think 2018. We'll be right back.