>> Narrator: Live from Las Vegas, it's theCUBE! Covering IBM Think 2018. Brought to you by IBM. >> Hello and I'm John Furrier. We're here in theCUBE studios at Think 2018, IBM Think 2018 in Mandalay Bay, in Las Vegas. We're extracting the signal from the noise, talking to all the executives, customers, thought leaders, inside the community of IBM and theCUBE. Our next guest is Ritika Gunnar who is the VP of Product for Watson and AI, cloud data platforms, all the goodness of the product side. Welcome to theCUBE. >> Thank you, great to be here again. >> So, we love talking to the product people because we want to know what the product strategy is. What's available, what's the hottest features. Obviously, we've been talking about, these are our words, Jenny introduced the innovation sandwich. >> Ritika: She did. >> The data's in the middle, and you have blockchain and AI on both sides of it. This is really the future. This is where they're going to see automation. This is where you're going to see efficiencies being created, inefficiencies being abstracted away. Obviously blockchain's got more of an infrastructure, futuristic piece to it. AI in play now, machine learning. You got Cloud underneath it all. How has the product morphed? What is the product today? We've heard of World of Watson in the past. You got Watson for this, you got Watson for IOT, You got Watson for this. What is the current offering? What's the product? Can you take a minute, just to explain what, semantically, it is? >> Sure. I'll start off by saying what is Watson? Watson is AI for smarter business. I want to start there. Because Watson is equal to how do we really get AI infused in our enterprise organizations and that is the core foundation of what Watson is. You heard a couple of announcements that the conference this week about what we're doing with Watson Studio, which is about providing that framework for what it means to infuse AI in our clients' applications. And you talked about machine learning. It's not just about machine learning anymore. It really is about how do we pair what machine learning is, which is about tweaking and tuning single algorithms, to what we're doing with deep learning. And that's one of the core components of what we're doing with Watson Studio is how do we make AI truly accessible. Not just machine learning but deep learning to be able to infuse those in our client environments really seamlessly and so the deep learning as a service piece of what we're doing in the studio was a big part of the announcements this week because deep learning allows our clients to really have it in a very accessible way. And there were a few things we announced with deep learning as a service. We said, look just like with predictive analytics we have capabilities that easily allow you to democratize that to knowledge workers and to business analysts by adding drag-and-drop capabilities. We can do the same thing with deep learning and deep learning capabilities. So we have taken a lot of things that have come from our research area and started putting those into the product to really bring about enterprise capabilities for deep learning but in a really de-skilled way. >> Yeah, and also to remind the folks, there's a platform involved here. Maybe you can say it's been re-platformed, I don't know. Maybe you can answer that. Has it been re-platformed or is it just the platformization of existing stuff? Because there's certainly demand. TensorFlow at Google showed that there's a demand for machine learning libraries and then deep learning behind. You got Amazon Web Services with Sagemaker, Touting. As a service model for AI, it's definitely in demand. So talk about the platform piece underneath. What is it? How does it get rendered? And then we'll come back and talk about the user consumption side. >> So it definitely is not a re-platformization. You recall what we have done with a focus initially on what we did on data science and what we did on machine learning. And the number one thing that we did was we were about supporting open-source and open frameworks. So it's not just one framework, like a TensorFlow framework, but it's about what we can do with TensorFlow, Keras, PyTorch, Caffe, and be able to use all of our builders' favorite open-source frameworks and be able to use that in a way where then we can add additional value on top of that and help them accelerate what it means to actually have that in the enterprise and what it means to actually de-skill that for the organization. So we started there. But really, if you look at where Watson has focused on the APIs and the API services, it's bringing together those capabilities of what we're doing with unstructured, pre-trained services, and then allowing clients to be able to bring together the structured and unstructured together on one platform, and adding the deep learning as a service capabilities, which is truly differentiating. >> Well, I think the important point there, just to amplify, and for the people to know is, it's not just your version of the tools for the data, you're looking at bringing data in from anywhere the customer, your customer wants it. And that's super critical. You don't want to ignore data. You can't. You got to have access to the data that matters. >> Yeah, you know, I think one of the other critical pieces that we're talking about here is, data without AI is meaningless and AI without data is really not useful or very accurate. So, having both of them in a yin yang and then bringing them together as we're doing in the Watson Studio is extremely important. >> The other thing I want get now to the user side, the consumption side you mentioned making it easier, but one of the things we've been hearing, that's been a theme in the hallways and certainly in theCUBE here is; bad data equals bad AI. >> Bad data equals bad AI. >> It's not just about bolting a AI on, you really got to take a holistic approach and a hygiene approach to the data and understanding where the data is contextually is relevant to the application. Talk about, that means kind of nuance, but break that down. What's your reaction to that and how do you talk to customers saying, okay look you want to do AI here's the playbook. How do you explain that in a very simple way? >> Well you heard of the AI ladder, making your data ready for AI. This is a really important concept because you need to be able to have trust in the data that you have, relevancy in the data that you have, and so it is about not just the connectivity to that data, but can you start having curated and rich data that is really valuable, that's accurate that you can trust, that you can leverage. It becomes not just about the data, but about the governance and the self-service capabilities that you can have and around that data and then it is about the machine learning and the deep learning characteristics that you can put on there. But, all three of those components are absolutely essential. What we're seeing it's not even about the data that you have within the firewall of your organization, it's about what you're doing to really augment that with external data. That's another area that we're having pre-trained, enriched, data sets with what we're doing with the Wats and data kits is extremely important; industry specific data. >> Well you know my pet peeve is always I love data. I'm a data geek, I love innovation, I love data driven, but you can't have data without good human interaction. The human component is critical and certainly with seeing trends where startups like Elation that we've interviewed; are taking this social approach to data where they're looking at it like you don't need to be a data geek or data scientist. The average business person's creating the value in especially blockchain, we were just talking in theCUBE that it's the business model Innovations, it's universal property and the technology can be enabled and managed appropriately. This is where the value is. What's the human component? Is there like... You want to know who's using the data? >> Well-- >> Why are they using data? It's like do I share the data? Can you leverage other people's data? This is kind of a melting pot. >> It is. >> What's the human piece of it? >> It truly is about enabling more people access to what it means to infuse AI into their organization. When I said it's not about re-platforming, but it's about expanding. We started with the data scientists, and we're adding to that the application developer. The third piece of that is, how do you get the knowledge worker? The subject matter expert? The person who understand the actual machine, or equipment that needs to be inspected. How do you get them to start customizing models without having to know anything about the data science element? That's extremely important because I can auto-tag and auto-classify stuff and use AI to get them started, but there is that human element of not needing to be a data scientist, but still having input into that AI and that's a very beautiful thing. >> You know it's interesting is in the security industry you've seen groups; birds of a feather flock together, where they share hats and it's a super important community aspect of it. Data has now, and now with AI, you get the AI ladder, but this points to AI literacy within the organizations. >> Exactly. >> So you're seeing people saying, hey we need AI literacy. Not coding per se, but how do we manage data? But it's also understanding who within your peer group is evolving. So your seeing now a whole formation of user base out there, users who want to know who their; the birds of the other feather flocking together. This is now a social gamification opportunity because they're growing together. >> There're-- >> What's your thought on that? >> There're two things there I would say. First, is we often go to the technology and as a product person I just spoke to you a lot about the technology. But, what we find in talking to our clients, is that it really is about helping them with the skills, the culture, the process transformation that needs to happen within the organization to break down the boundaries and the silos exist to truly get AI into an organization. That's the first thing. The second, is when you think about AI and what it means to actually infuse AI into an enterprise organization there's an ethics component of this. There's ethics and bias, and bias components which you need to mitigate and detect, and those are real problems and by the way IBM, especially with the work that we're doing within Watson, with the work that we're doing in research, we're taking this on front and center and it's extremely important to what we do. >> You guys used to talk about that as cognitive, but I think you're so right on. I think this is such a progressive topic, love to do a deeper dive on it, but really you nailed it. Data has to have a consensus algorithm built into it. Meaning you need to have, that's why I brought up this social dynamic, because I'm seeing people within organizations address regulatory issues, legal issues, ethical, societal issues all together and it requires a group. >> That's right. >> Not just algorithm, people to synthesize. >> Exactly. >> And that's either diversity, diverse groups from different places and experiences whether it's an expert here, user there; all coming together. This is not really talked about much. How are you guys-- >> I think it will be more. >> John: It will, you think so? >> Absolutely it will be more. >> What do you see from customers? You've done a lot of client meetings. Are they talking about this? Or they still more in the how do I stand up AI, literacy. >> They are starting to talk about it because look, imagine if you train your model on bad data. You actually have bias then in your model and that means that the accuracy of that model is not where you need it to be if your going to run it in an enterprise organization. So, being able to do things like detect it and proactively mitigate it are at the forefront and by the way this where our teams are really focusing on what we can do to further the AI practice in the enterprise and it is where we really believe that the ethics part of this is so important for that enterprise or smarter business component. >> Iterating through the quality the data's really good. Okay, so now I was talking to Rob Thomas talking about data containers. We were kind of nerding out on Kubernetes and all that good stuff. You almost imagine Kubernetes and containers making data really easy to move around and manage effectively with software, but I mentioned consensus on the understanding the quality of the data and understanding the impact of the data. When you say consensus, the first thing that jumps in my mind is blockchain, cryptocurrency. Is there a tokenization economics model in data somewhere? Because all the best stuff going on in blockchain and cryptocurrency that's technically more impactful is the changing of the economics. Changing of the technical architectures. You almost can say, hmm. >> You can actually see over a time that there is a business model that puts more value not just on the data and the data assets themselves, but on the models and the insights that are actually created from the AI assets themselves. I do believe that is a transformation just like what we're seeing in blockchain and the type of cryptocurrency that exists within there, and the kind of where the value is. We will see the same shift within data and AI. >> Well, you know, we're really interested in exploring and if you guys have any input to that we'd love to get more access to thought leaders around the relationship people and things have to data. Obviously the internet of things is one piece, but the human relationship the data. You're seeing it play out in real time. Uber had a first death this week, that was tragic. First self-driving car fatality. You're seeing Facebook really get handed huge negative press on the fact that they mismanaged the data that was optimized for advertising not user experience. You're starting to see a shift in an evolution where people are starting to recognize the role of the human and their data and other people's data. This is a big topic. >> It's a huge topic and I think we'll see a lot more from it and the weeks, and months, and years ahead on this. I think it becomes a really important point as to how we start to really innovate in and around not just the data, but the AI we apply to it and then the implications of it and what it means in terms of if the data's not right, if the algorithm's aren't right, if the biases is there. It is big implications for society and for the environment as a whole. >> I really appreciate you taking the time to speak with us. I know you're super busy. My final question's much more share some color commentary on IBM Think this week, the event, your reaction to, obviously it's massive, and also the customer conversations you've had. You've told me that your in client briefings and meetings. What are they talking about? What are they asking for? What are some of the things that are, low-hanging fruit use cases? Where's the starting point? Where are people jumping in? Can you just share any data you have on-- >> Oh I can share. That's a fully loaded question; that's like 10 questions all in one. But the Think conference has been great in terms of when you think about the problems that we're trying to solve with AI, it's not AI alone, right? It actually is integrated in with things like data, with the systems, with how we actually integrate that in terms of a hybrid way of what we're doing on premises and what we're doing in private Cloud, what we're doing in public Cloud. So, actually having a forum where we're talking about all of that together in a unified manner has actually been great feedback that I've heard from many customers, many analysts, and in general from an IBM perspective, I believe has been extremely valuable. I think the types of questions that I'm hearing and the types of inputs and conversations we're having, are one of where clients want to be able to innovate and really do things that are in Horizon three type things. What are the things they should be doing in Horizon one, Horizon two, and Horizon three when it comes to AI and when it comes to AI and how they treat their data. This is really important because-- >> What's Horizon one, two and three? >> You think about Horizon one, those are things you should be doing immediately to get immediate value in your business. Horizon two, are kind of mid-term, 18 to 24. 24 plus months out is Horizon 3. So when you think about an AI journey, what is your AI journey really look like in terms of what you should be doing in the immediate terms. Small, quick wins. >> Foundational. >> What are things that you can do kind of projects that will pan out in a year and what are the two to three year projects that we should be doing. This are the most frequent conversations that I've been having with a lot of our clients in terms of what is that AI journey we should be thinking about, what are the projects right now, how do we work with you on the projects right now on H1 and H2. What are the things we can start incubating that are longer term. And these extremely transformational in nature. It's kind of like what do we do to really automate self-driving, not just cars, but what we do for trains and we do to do really revolutionize certain industries and professions. >> How does your product roadmap to your Horizons? Can you share a little bit about the priorities on the roadmap? I know you don't want to share a lot of data, competitive information. But, can you give an antidotal or at least a trajectory of what the priorities are and some guiding principals? >> I hinted at some of it, but I only talked about the Studio, right... During this discussion, but still Studio is just one of a three-pronged approach that we have in Watson. The Studio really is about laying the foundation that is equivalent for how do we get AI in our enterprises for the builders, and it's like a place where builders go to be able to create, build, deploy those models, machine learning, deep learning models and be able to do so in a de-skilled way. Well, on top of that, as you know, we've done thousands of engagements and we know the most comprehensive ways that clients are trying to use Watson and AI in their organizations. So taking our learnings from that, we're starting to harden those in applications so that clients can easily infuse that into their businesses. We have capabilities for things like Watson Assistance, which was announced this week at the conference that really helped clients with pre-existing skills like how do you have a customer care solution, but then how can you extend it to other industries like automotive, or hospitality, or retail. So, we're working not just within Watson but within broader IBM to bring solutions like that. We also have talked about compliance. Every organization has a regulatory, or compliance, or legal department that deals with either SOWs, legal documents, technical documents. How do you then start making sure that you're adhering to the types of regulations or legal requirements that you have on those documents. Compare and comply actually uses a lot of the Watson technologies to be able to do that. And scaling this out in terms of how clients are really using the AI in their business is the other point of where Watson will absolutely focus going forward. >> That's awesome, Ritika. Thank you for coming on theCUBE, sharing the awesome work and again gutting across IBM and also outside in the industry. The more data the better the potential. >> Absolutely. >> Well thanks for sharing the data. We're putting the data out there for you. theCUBE is one big data machine, we're data driven. We love doing these interviews, of course getting the experts and the product folks on theCUBE is super important to us. I'm John Furrier, more coverage for IBM Think after this short break. (upbeat music)