 Live from San Francisco, California. It's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. We're back at Fisherman's Wharf at the IBM CDO conference. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. Joe Selly is here. He's the Global Advanced Analytics and Cognitive Lead at IBM, Boston Base. Joe, good to see you again. And Jerry Gupta, the Senior Vice President and Digital Catalyst at Swiss Re-Institute Great to see you, thanks for coming on. Thank you for having me, Dave. You're very welcome. So Jerry, you've been at this event now a couple of years. We've been here, I think the last four or five years. And in the early, now this goes back 10 years, this event. Now 10 years ago, it was kind of before the whole big data meme took off. It was a lot of focus, I'm sure, on data quality and data compliance. Then all of a sudden data became the new source of value. And then we rolled into digital transformation. But from your perspective, how have things changed? Maybe the themes over the last couple of years, how have they changed? I think from a team perspective, I would frame the question a little differently, right? For me, this conference is a must-have on my calendar because it's very relevant. The topics are very current. So two years ago when I first attended this conference, it was about cyber. And when you went out in the market, there were not too many companies talking about cyber. And so you come to a place like this and you're sort of blown away by the depth of knowledge that IBM has, the statistics that you guys did a great job presenting. And that really helped us inform ourselves about the cyber risk it was going on cyber. And so it's evolved a little bit. The consistent theme is it's relevant, it's topical. The other thing that's very consistent is that you always learn something new. The struggle with large conferences like this is sometimes it becomes a lot of me-to-environment. But in conference that IBM organizes the CDO in particular, I always learn something new because of practitioners. They do a really good job curating the practice. And Joe, this has always been an intimate event. You do them in San Francisco and Boston. It's a couple hundred people, really kind of belly-to-belly interactions. So that's kind of nice. But how do you scale this globally? Well, I would say that is the key question because I think the AI algorithms and the machine learning has been proven to work. And we've infiltrated that into all of the business processes at IBM and in many of our client companies. But we've been doing proof of concepts and small applications and maybe there's a dozen or 50 people using it. But the themes now are around scale. AI at scale, how do you do that? We have a remit at IBM to get 100,000 IBMers, that's the real number, on our cognitive enterprise data platform by the end of this calendar year. And we're making great progress there, but that's the key question, how do you do that? And it involves cultural issues of teams and business process owners being willing to share the data, which is really key. And it also involves technical issues around cloud computing models, hybrid public and private clouds, multi-cloud environments where we know we're not the only game in town, so there's a Microsoft cloud, there's an IBM cloud, there's another cloud. And all of those clouds have to be woven together in some sort of a multi-cloud management model. So that's the techie geek part, but the cultural change part is equally as challenging and important and you need both to get to 100,000 users at IBM. You know guys, what this conversation brings into focus for me is that for decades we've marched to the cadence of Moore's Law as the innovation engine for our industry. That feels like just so yesterday. Today it's like you've got this data bedrock that we built up over the last decade. You've got machine intelligence or AI that you now can apply to that data, and then for scale you've got cloud. And there's all kinds of innovation coming in. Does that sort of innovation cocktail or sandwich make sense in your business? So there's the innovation piece of it, which is new and exciting, the shiny new toy. And that's definitely exciting and we definitely track that. But from my perspective and the perspective of my company, it's not the shiny new toy that's attractive or that really moves me for us. It is the underlying risk. So if you have the shiny new toy of an autonomous vehicle, what mayhem is it going to cause? What are the underlying risks? That's what we are focused on. And Joe alluded to AI and algorithms and stuff. And it clearly is a very, it's starting to become a very big topic globally when people are starting to talk about the risks and dangers inherent in algorithms and AI. And for us that's an opportunity that we need to study more, look into deeply to see if this is something that we can help address and solve. So you're looking for blind spots essentially. And then one of them is this sort of algorithmic risk. Is that the right way to look at it? How do you think about risk of algorithms? So yes, so algorithmic risk would be our blind spot. I think that's a really good way of saying it. We look at not just blind spots. So risks that we don't even know about that we are facing, we also look at known risks. So we are one of the largest reinsurers in the world and you name a risk, we reinsure it. So your auto risk, your catastrophe risk, you name it, we probably have some exposure to it. The blind spot as you call it are, any time you create something new, there are pros and cons. The shiny new toy is the pro. What risks, what damage, what liability can result there in. That's the piece that we are starting to look at. So you got to potentially show these unintended consequences of algorithms. So how do you address that? Is there a way in which you've thought through some kind of oversight of the algorithms? Maybe you could talk about IBM's point of view there. And that's a fantastic and interesting conversation that Jerry and I are having together on behalf of our organizations. IBM knowing in great detail about how these AI algorithms work and are built and are deployed. Jerry and his organization knowing the bigger risk picture and how you understand, predict, remediate and protect against the risk so that so the companies can happily adopt these new technologies and put them everywhere in their business. So the name of the game is really understanding how as we all move towards a digital enterprise with big data streaming in in every format. So we use AI to modify the data, to train the models and then we set some of the models up as self training. So they're learning on their own. They're enhancing data sets. And once we turn them on, we can go to sleep. So they do their own thing. Then what? We need a way to understand how these models are producing results. Are they results that we agree with? Are these self training algorithms making these like railroad trains going off the track or are they still on the track? So we want to monitor, understand and remediate. But it's at scale again, my earlier comments. So you might be an organization, you might have 10,000 models at work. You can't watch those. So you're looking at the intersection of risk and machine intelligence. And then you're, if I understand it correctly, applying AI, what I call machine intelligence to oversee the algorithms, is that correct? Well, yes. And you could think of it as an AI watching over the other AI. That's really what we have. Because we're using AI as we envision what might or might not be the future. It's an AI and it's watching other AI. That's kind of mind blowing. Jerry, you mentioned autonomous vehicles before. That's obviously a potential disrupter to your business. What can you share about how you guys are thinking about that? I mean, a lot of people are skeptical. Like there's not enough data. Every time there's another accident, they'll point to that. What's your point of view on that from your corporation standpoint? Are you guys thinking it's near term, mid term, very long term? Or it's sort of this journey that there's quasi-autonomous that sort of gets us there. So on autonomous vehicles or algorithmic vehicles? On autonomous vehicles. So, you know, the journey towards full automation is a series of continuous steps, right? So it's a continuum. And to a certain extent, we are in a space now where even though we may not have full autonomy while we're driving, there are significant feedback and signals that a car provides and acts in an automated manner that eventually moves towards full autonomy. Like for example, the anti-lock braking system. That's a component of that, right? Which is it prevents the car from skidding out of control. So if you're asking for a time horizon when it might happen, at a previous firm we had done some analysis and the horizons were as sort of aggressive as 15 years to as conservative as 50 years. But the component that we all agreed to where there was not such a wide range was that the cars are becoming more sophisticated. Because the cars, well, not just cars, any automobile or truck vehicles, they're becoming more automated. Where does risk lie at each piece or each piece of the value chain, right? And the answer is different if you look at commercial versus personal. If you look at commercial space, autonomous fleets are already on the road, right? And so the question then becomes where does liability lie? Owner, manufacturer, driver. Shared model there. You know, manual versus automated mode, conditions of driving, what decisions algorithm is making, which is when you know the physics don't allow you to avoid an accident, who do you end up hitting? Again, not just a technology problem. Now, last thing is you guys are doing a panel on wowing customers, making customers the king, I think, is what the title of it is. What's that all about? And get into that a little bit. Sure, well, we focus as IBM mostly on a B2B framework. So the example that I'll share to you is somewhere between like making a customer or making a client the king. But the example is that we're using some of our AI to create an alert system that we call operations risk insights. And so the example that I wanted to share was that we've been giving this away to non-profit relief agencies who can deploy it around a geo-fenced area like say North Carolina and South Carolina. And if your relief agency providing flood relief or services to people affected by floods, you can use our solution to understand the magnitude and the potential damage impact from a storm. We can layer up a map with not only normal geospatial information, but socioeconomic data so I can say if I'm the relief agency and I've got a huge storm coming in and I can't cover the entire two state area, I can say, okay, well show me the area where there's greater population density than 1,000 per square kilometer and the socioeconomic level is lower than a certain point. And those are the people that don't have a lot of resources, can't move, are going to shelter in place. So I want to know that because they need my help. That's where the risk is, yeah. And we use AI to do, to use that. Those are happy customers and I've delivered wow to them. That's pretty wow. That's pretty, Jerry, anything you would add to that? Sort of wow customer experience? Yeah, absolutely. So we are a B2B company as well. And so the span of interaction is dictated by that piece of our business. And so we try to create wow by either making our customers' life easier, providing tools and technologies that make them do their jobs better, cheaper, faster, more efficiently. Or by helping create, co-create, modify our products, such that it accomplishes the former, right? So Joe mentioned about the product that you launched. So we have what we call parametric insurance and we have one of the pioneers in the field. And so we've launched three products in that area for earthquake, for hurricanes, and for flight delay. And so, for example, our flight delay product is really unique in the market where we are able to ensure a traveler for flight delays. And then if there is a flight delay event that exceeds pre-established thresholds, the customer gets paid without even having to file a claim. I love that product. I want to learn more about it because you can just, you can just say, oh, force majeure. But then it's like, it's not a wow experience for the customer. Nobody's happy. So that's the thing, guys, we're out of time. We got to leave it there. Jerry, Joe, thanks so much for, let's do that down the road. Maybe we have you guys in Boston in the fall. It would be great. Thanks again for coming on. All right, and keep it right there, everybody. We'll be back with our next guest. You're watching theCUBE live from IBM CDO in San Francisco. Right back.