(upbeat music) >> (narrator) Live from Barcelona, Spain, it's theCUBE covering Cisco Live! Europe. Brought to you by Cisco and its ecosystem partners. >> Welcome back to theCUBE's live coverage of Cisco Live! 2019 here in Barcelona, Spain. I'm Stu Miniman and my cohost, Dave Vellante and John Furrs here with us. Wall to wall coverage going through all the areas of what Cisco's covering. Their transformation, become more of a software company. To help us dig into a very exciting area. We have Nicola Rohrseitz, who's the lead strategic AI program at Cisco. Nicola, thanks so much for joining us. >> Nice to meet you. >> Alright so AI is something that's pervading everything that we talk about. We definitely have the buzz and the hype in the industry. You sit at the nexus of all the different areas inside of Cisco so give us a little bit about your role inside the company. You've been there about two years and kind of the scope of what you do there. >> AI/ML has a long history inside of Cisco. We have not been very vocal about it, but it's being used throughout the company, and we once put together a map of all these things, this was one of my first activities, and I went, "Wow it's amazing." In all of these products we have some element of AI/ML. And this showed also that we have a very pragmatic approach to AI/ML. It's not this killer robots or you name it, it's more like okay, how do we use this to solve specific problems. >> Yep so, I think back, definitely analytics, when you talk about networking and flows, there's always been lots of data, and I've had tools to be able to access there. When I talk to most people in the industry though, it definitely is something new and different. You know, AI is not new. We've been talking about artificial intelligence for about 150 years. Machine learning, there's been movements on there. So maybe you can give us... What's the same as what we've had before, and what's new and different about the era that we're in today and the product that Cisco's bringing to market. >> It's a spectrum. On one side you have analytics, on the other side you have AI. And basically the fundamental difference is that the analytics usually accept that it's a person that creates the rules that draws the rules. On your hand the AI it's the computer that draws the rules. So in between those there is a gray zone in which you evolve and there's more and more done by the machine. But it really depends from the specific area in which you want to apply it. It is true, we are moving more and more towards artificial intelligence, more done by the machine. And the reason is clear, we have more and more data and up to a point in which AI will be the only option to do business because everything needs to be automated. >> I want to ask you as an AI expert. When I talk to security experts I always ask them, "Who is your favorite super hero?" because when they were little kids, they dreamt about saving the world. So as an AI expert, did you think about artificial intelligence or whatever you called it back then as a child? How'd you get into and interested in artificial intelligence? >> I've been always fascinated by how the brain works. I did my PhD in neuroscience and physics because of that. When I was a kid suddenly I thought, how do we think? How is this possible that we create stories in our mind and we dream at night and wake up. Then slowly little by little I kept on asking the question, How do you make this into a technical solution? How do you engineer something like this? And started looking into computers, well it's not like our brain works, so there's a difference. And now we are sort of like coming slowly together despite having started from very different paths from the Norman machines and so on how we are moving more and more to brain-inspired technologies. >> So you're seeing those two worlds come together. I was under the impression that despite that vision that today's AI anyway is really a lot about maybe automating processes, robotic process automation as an example, but you're talking about a world that much more mimics the human brain as we understand how the human brain works. Is that correct? >> It is correct in the sense that allow you to mimic certain fundamental capabilities. So intelligence is about receiving information, storing knowledge, thinking and adapting. You need all these four components to create a truly intelligence system, and you don't need to replicate individual neurons to make this happen, but at least understand the fundamental principle behind it, what's the computation like? And as you go along, because we are in a business we need to find complete solutions to business challenges and therefore we apply whatever we need from these principles to make something out of it. >> What are the things that humans can do today that computers have trouble doing? And how is that changing? >> One of the clearest thing is that computers are not able to think. They is an executing machine. They don't have a representation of what it means to do whatever they are doing to solve their problems. One of the next steps which the researchers are very interested in now is trying to understand the context in which a machine operates. Now if you ask a machine to do a certain task it can fail miserably because it's not able to connect the dots between different elements of the context. Part of the reason is that context, contextual information is so broad and large so much data, so which one do you pick? This is still an unsolved problem. >> Nicola, help us understand how we should think about Cisco when it comes to AI. People hear about Facebook, Google, IBM with their Watson pieces there. Obviously things like scale of networks, managing infrastructure and moving to some of these multi-cloud environment theme a natural fit for Cisco. But how should I as a user be thinking of, when do I come to Cisco? How does AI and ML fit into what Cisco does compared to some of those other software and enterprise IT providers? >> So doing AI at Cisco is super exciting because it's still an open field. AI/ML for networking is something that has not been solved yet and there are other areas where other companies operate in, they are much more advanced. Well for us there is lots of room to innovate. For us it's a business opportunity. It's a tremendous business opportunity. Some market research talks about 1.2 trillion dollars that's going to be captured by companies that adopt AI compared with those that don't, but for Cisco is really a necessity, because data is going to flow more and more through our networks. How do we handle that? What people don't realize in general compared to what's out there that ML for networking is a different beast. For one th6e data is different and often it's encrypted. So how do you do AI on encrypted data? And every network is unique. These are two fundamental differences that force us to be creative and to pioneer new ways of doing AI. It is super exciting. >> Does open source play into the activities that your different product groups are working on? >> In general AI has been driven by a very lively AI committee in the open source world, and then the question comes when we talk to partners and customers, How do we bring these solutions to production? Because certain packages of open source cannot be applied directly and this is one of the main pinpoints of the IT teams and the scientists and the engineers. >> I want to ask you about the black box phenomenon. As a human I can look at a dog and I can see it's a dog immediately but I can't really explain how I know it's a dog. I can, but I could be describing another animal. Computers can figure out but we don't really know exactly. It's like sort of a black box inside. Is that a problem? Do we need to make AI more transparent? Or is it increasingly going to be a black box that we just trust? What are your thoughts on that? >> It depends on the situation. You came here by plane. Do you know exactly how the plane works? I don't. I sort of know the principles, but I trust the industry, the regulations, everything that they have checked off everything. To me it is a sort of black box. However if there are certain things that I have to go under like surgery, I want to exactly what is going on. The same thing here in AI so there's the black box phenomenon. You don't know exactly how does this work. On one side I understand it and it makes sense you want to be sure that you know what's going on On the other hand sometimes you want a result and you don't really care about exactly how it works because ultimately the risk is not that high. And so you have to really think about what kind of risk management, how deep you want to look into it. The problem of transparency has been researched a lot because of course there's certain phenomena that touch the social sphere and there we have to be careful. When it touches private data, how is private data handled all that is very important of course. >> Yeah Stu and I often when we do these conversations John as well, we often ask ourselves, How far can we take AI and how far should we take AI so maybe a couple of examples if I may. Do you expect that within the next 10-15 years that machines will make better diagnosis than doctors? >> Oh, they already do. >> (Dave) They already do. >> New research has been shown that in certain cases specific cases that they have better accuracy. However to bring that again into production at the level that we go to the hospital and there's a machine to help us diagnose, well we are still at least some years away because there's a process of certification and it must be added that on one side, it's really about augmented intelligence rather than artificial intelligence. The machines will help us diagnose but then the responsibility should stay with the human. >> Another question we like to ask is around automobiles. Do you think it will become the exception rather than the rule, that individuals will own and drive their own automobiles? >> It's going to be the exception in the future that is going be no ownership and driving, active driving. It's going to, it's interesting because it's going to become like when it started, a pleasure to drive. You drive because you want to drive. You going to drive those hills up and down and really enjoy it. Otherwise you go on your commute, well you have work to do. >> I still have a stick shift. >> You going to enjoy it. >> I got to ask you, so the likes of Elon Musk and Hawkings have said, you know, projected that AI is a bad thing, it's going to, machines are going to take over the world. I don't sense that you're of that mindset, but what are your thoughts on that, those dire predictions. Are they ultimately going to come true? Do you feel like they're over-blown or who knows? It's hard to forecast, but what are your thoughts on that? >> It's important to acknowledge these forecasts of dire future because AI is capable of lots of things at scale and this is the key differentiator. So whatever you can do, you can do it at scale automatically things on their own. So it's more than predicting our future is like I'm wanting to say developers, managers, be careful of your choices because they're going to have an affect at scale. And this is not just an AI effect, It's really a technology effect. if you look at AI today, there are lots of pieces that come together, lots of pieces that come also from the big data era, and now they are being transformed and you add a little bit of AI in the mix but to make it to work there's a lot around it. So AI is the culprit because of the science fiction history and everything. But ultimately the ability to do things at scale automatically, that is really where we have to be careful. >> So Nicola, what should we be looking for, when we watch Cisco going forward for the next couple of years in this space. What are some key milestones that you think will come to reality? >> We are going to release products that have more and more AI into it and the whole industry will evolve and have a better understanding of what's possible and what not. AI in Cisco revolves around three axis. One infrastructure, two the fit, and unique data. Infrastructure is how do we deal with increase of data create this future-proof networks. This is like our core business. The two the fit is that we provide entrance solutions for our customers and partners so that they can implement their AI/ML strategies and this is a really interesting topic because AI/ML is a moving enterprise and other organizations but it's still in the early stages because of all these operational challenges which we at Cisco are very good at solving. The third point is unique data. Unique in terms of volume, breadth and type of data. This is where on one side we have systems that work at scale but also we have the kind of data that can be used by our customers to better understand their own business. >> Nicola we really appreciate you giving us a nice overview of all the areas for AI. Dave Vellante and I are still humans here doing the interviews here until the robots take over all of our jobs. Until then thanks as always for watching theCUBE. (upbeat music)