>> Announcer: From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now here's your host, Dave Vellante. >> In the early days of big data and Hadoop, the focus was really on operational efficiency where ROI was largely centered on reduction of investment. Fast forward 10 years and you're seeing a plethora of activity around machine learning, and deep learning, and artificial intelligence, and deeper business integration as a function of machine intelligence. Welcome to this Cube conversation, The Skinny on Machine Intelligence. I'm Dave Vellante and I'm excited to have Jim Kobielus here up from the District area. Jim, great to see you. Thanks for coming into the office today. >> Thanks a lot, Dave, yes great to be here in beautiful Marlboro, Massachusetts. >> Yes, so you know Jim, when you think about all the buzz words in this big data business, I have to ask you, is this just sort of same wine, new bottle when we talk about all this AI and machine intelligence stuff? >> It's actually new wine. But of course there's various bottles and they have different vintages, and much of that wine is still quite tasty, and let me just break it out for you, the skinny on machine intelligence. AI as a buzzword and as a set of practices really goes back of course to the early post-World War II era, as we know Alan Turing and the Imitation Game and so forth. There are other developers, theorists, academics in the '40s and the '50s and '60s that pioneered in this field. So we don't want to give Alan Turing too much credit, but he was clearly a mathematician who laid down the theoretical framework for much of what we now call Artificial Intelligence. But when you look at Artificial Intelligence as a ever-evolving set of practices, where it began was in an area that focused on deterministic rules, rule-driven expert systems, and that was really the state of the art of AI for a long, long time. And so you had expert systems in a variety of areas that became useful or used in business, and science, and government and so forth. Cut ahead to the turn of the millennium, we are now in the 21st century, and what's different, the new wine, is big data, larger and larger data sets that can reveal great insights, patterns, correlations that might be highly useful if you have the right statistical modeling tools and approaches to be able to surface up these patterns in an automated or semi-automated fashion. So one of the core areas is what we now call machine learning, which really is using statistical models to infer correlations, anomalies, trends, and so forth in the data itself, and machine learning, the core approach for machine learning is something called Artificial Neural Networks, which is essentially modeling a statistical model along the lines of how, at a very high level, the nervous system is made up, with neurons connected by synapses, and so forth. It's an analog in statistical modeling called a perceptron. The whole theoretical framework of perceptrons actually got started in the 1950s with the first flush of AI, but didn't become a practical reality until after the turn of this millennium, really after the turn of this particular decade, 2010, when we started to see not only very large big data sets emerge and new approaches for managing it all, like Hadoop, come to the fore. But we've seen artificial neural nets get more sophisticated in terms of their capabilities, and a new approach for doing machine learning, artificial neural networks, with deeper layers of perceptrons, neurons, called deep learning has come to the fore. With deep learning, you have new algorithms like convolutional neural networks, recurrent neural networks, generative adversarial neural networks. These are different ways of surfacing up higher level abstractions in the data, for example for face recognition and object recognition, voice recognition and so forth. These all depend on this new state of the art for machine learning called deep learning. So what we have now in the year 2017 is we have quite a mania for all things AI, much of it is focused on deep learning, much of it is focused on tools that your average data scientist or your average developer increasingly can use and get very productive with and build these models and train and test them, and deploy them into working applications like going forward, things like autonomous vehicles would be impossible without this. >> Right, and we'll get some of that. But so you're saying that machine learning is essentially math that infers patterns from data. And math, it's new math, math that's been around for awhile or. >> Yeah, and inferring patterns from data has been done for a long time with software, and we have some established approaches that in many ways predate the current vogue for neural networks. We have support vector machines, and decision trees, and Bayesian logic. These are different ways of approaches statistical for inferring patterns, correlations in the data. They haven't gone away, they're a big part of the overall AI space, but it's a growing area that I've only skimmed the surface of. >> And they've been around for many many years, like SVM for example. Okay, now describe further, add some color to deep learning. You sort of painted a picture of this sort of deep layers of these machine learning algorithms and this network with some depth to it, but help us better understand the difference between machine learning and deep learning, and then ultimately AI. >> Yeah, well with machine learning generally, you know, inferring patterns from data that I said, artificial neural networks of which the deep learning networks are one subset. Artificial neural networks can be two or more layers of perceptrons or neurons, they have relationship to each other in terms of their activation according to various mathematical functions. So when you look at an artificial neural network, it basically does very complex math equations through a combination of what they call scalar functions, like multiplication and so forth, and then you have these non-linear functions, like cosine and so forth, tangent, all that kind of math playing together in these deep structures that are triggered by data, data input that's processed according to activation functions that set weights and reset the weights among all the various neural processing elements, that ultimately output something, the insight or the intelligence that you're looking for, like a yes or no, is this a face or not a face, that these incoming bits are presenting. Or it might present output in terms of categories. What category of face is this, a man, a woman, a child, or whatever. What I'm getting at is that so deep learning is more layers of these neural processing elements that are specialized to various functions to be able to abstract higher level phenomena from the data, it's not just, "Is this a face," but if it's a scene recognition deep learning network, it might recognize that this is a face that corresponds to a person named Dave who also happens to be the father in the particular family scene, and by the way this is a family scene that this deep learning network is able to ascertain. What I'm getting at is those are the higher level abstractions that deep learning algorithms of various sorts are built to identify in an automated way. >> Okay, and these in your view all fit under the umbrella of artificial intelligence, or is that sort of an uber field that we should be thinking of. >> Yeah, artificial intelligence as the broad envelope essentially refers to any number of approaches that help machines to think like humans, essentially. When you say, "Think like humans," what does that mean actually? To do predictions like humans, to look for anomalies or outliers like a human might, you know separate figure from ground for example in a scene, to identify the correlations or trends in a given scene. Like I said, to do categorization or classification based on what they're seeing in a given frame or what they're hearing in a given speech sample. So all these cognitive processes just skim the surface, or what AI is all about, automating to a great degree. When I say cognitive, but I'm also referring to affective like emotion detection, that's another set of processes that goes on in our heads or our hearts, that AI based on deep learning and so forth is able to do depending on different types of artificial neural networks are specialized particular functions, and they can only perform these functions if A, they've been built and optimized for those functions, and B, they have been trained with actual data from the phenomenon of interest. Training the algorithms with the actual data to determine how effective the algorithms are is the key linchpin of the process, 'cause without training the algorithms you don't know if the algorithm is effective for its intended purpose, so in Wikibon what we're doing is in the whole development process, DevOps cycle, for all things AI, training the models through a process called supervised learning is absolutely an essential component of ascertaining the quality of the network that you've built. >> So that's the calibration and the iteration to increase the accuracy, and like I say, the quality of the outcome. Okay, what are some of the practical applications that you're seeing for AI, and ML, and DL. >> Well, chat bots, you know voice recognition in general, Siri and Alexa, and so forth. Without machine learning, without deep learning to do speech recognition, those can't work, right? Pretty much in every field, now for example, IT service management tools of all sorts. When you have a large network that's logging data at the server level, at the application level and so forth, those data logs are too large and too complex and changing too fast for humans to be able to identify the patterns related to issues and faults and incidents. So AI, machine learning, deep learning is being used to fathom those anomalies and so forth in an automated fashion to be able to alert a human to take action, like an IT administrator, or to be able to trigger a response work flow, either human or automated. So AI within IT service management, hot hot topic, and we're seeing a lot of vendors incorporate that capability into their tools. Like I said, in the broad world we live in in terms of face recognition and Facebook, the fact is when I load a new picture of myself or my family or even with some friends or brothers in it, Facebook knows lickity-split whether it's my brother Tom or it's my wife or whoever, because of face recognition which obviously depends, well it's not obvious to everybody, depends on deep learning algorithms running inside Facebook's big data network, big data infrastructure. They're able to immediately know this. We see this all around us now, speech recognition, face recognition, and we just take it for granted that it's done, but it's done through the magic of AI. >> I want to get to the development angle scenario that you specialize in. Part of the reason why you came to Wikibon is to really focus on that whole application development angle. But before we get there, I want to follow the data for a bit 'cause you mentioned that was really the catalyst for the resurgence in AI, and last week at the Wikibon research meeting we talked about this three-tiered model. Edge, as edge piece, and then something in the middle which is this aggregation point for all this edge data, and then cloud which is where I guess all the deep modeling occurs, so sort of a three-tier model for the data flow. >> John: Yes. >> So I wonder if you could comment on that in the context of AI, it means more data, more I guess opportunities for machine learning and digital twins, and all this other cool stuff that's going on. But I'm really interested in how that is going to affect the application development and the programming model. John Farrier has a phrase that he says that, "Data is the new development kit." Well, if you got all this data that's distributed all over the place, that changes the application development model, at least you think it does. So I wonder if you could comment on that edge explosion, the data explosion as a result, and what it means for application development. >> Right, so more and more deep learning algorithms are being pushed to edge devices, by that I mean smartphones, and smart appliances like the ones that incorporate Alexa and so forth. And so what we're talking about is the algorithms themselves are being put into CPUs and FPGAs and ASICs and GPUs. All that stuff's getting embedded in everything that we're using, everything's that got autonomous, more and more devices have the ability if not to be autonomous in terms of making decisions, independent of us, or simply to serve as augmentation vehicles for our own whatever we happen to be doing thanks to the power of deep learning at the client. Okay, so when deep learning algorithms are embedded in say an internet of things edge device, what the deep learning algorithms are doing is A, they're ingesting the data through the sensors of that device, B, they're making inferences, deep learning algorithmic-driven inferences, based on that data. It might be speech recognition, face recognition, environmental sensing and being able to sense geospatially where you are and whether you're in a hospitable climate for whatever. And then the inferences might drive what we call actuation. Now in the autonomous vehicle scenario, the autonomous vehicle is equipped with all manner of sensors in terms of LiDAR and sonar and GPS and so forth, and it's taking readings all the time. It's doing inferences that either autonomously or in conjunction with inferences that are being made through deep learning and machine learning algorithms that are executing in those intermediary hubs like you described, or back in the cloud, or in a combination of all of that. But ultimately, the results of all those analytics, all those deep learning models, feed the what we call actuation of the car itself. Should it stop, should it put on the brakes 'cause it's about to hit a wall, should it turn right, should it turn left, should it slow down because it happened to have entered a new speed zone or whatever. All of the decisions, the actions that the edge device, like a car would be an edge device in this scenario, are being driven by evermore complex algorithms that are trained by data. Now, let's stay with the autonomous vehicle because that's an extreme case of a very powerful edge device. To train an autonomous vehicle you need of course lots and lots of data that's acquired from possibly a prototype that you, a Google or a Tesla, or whoever you might be, have deployed into the field or your customers are using, B, proving grounds like there's one out by my stomping ground out in Ann Arbor, a proving ground for the auto industry for self-driving vehicles and gaining enough real training data based on the operation of these vehicles in various simulated scenarios, and so forth. This data is used to build and iterate and refine the algorithms, the deep learning models that are doing the various operations of not only the vehicles in isolation but the vehicles operating as a fleet within an entire end to end transportation system. So what I'm getting at, is if you look at that three-tier model, then the edge device is the car, it's running under its own algorithms, the middle tier the hub might be a hub that's controlling a particular zone within a traffic system, like in my neck of the woods it might be a hub that's controlling congestion management among self-driving vehicles in eastern Fairfax County, Virginia. And then the cloud itself might be managing an entire fleet of vehicles, let's say you might have an entire fleet of vehicles under the control of say an Uber, or whatever is managing its own cars from a cloud-based center. So when you look at the tiering model that analytics, deep learning analytics is being performed, increasingly it will be for various, not just self-driving vehicles, through this tiered model, because the edge device needs to make decisions based on local data. The hub needs to make decisions based on a wider view of data across a wider range of edge entities. And then the cloud itself has responsibility or visibility for making deep learning driven determinations for some larger swath. And the cloud might be managing both the deep learning driven edge devices, as well as monitoring other related systems that self-driving network needs to coordinate with, like the government or whatever, or police. >> So envisioning that three-tier model then, how does the programming paradigm change and evolve as a result of that. >> Yeah, the programming paradigm is the modeling itself, the building and the training and the iterating the models generally will stay centralized, meaning to do all these functions, I mean to do modeling and training and iteration of these models, you need teams of data scientists and other developers who are both adept as to statistical modeling, who are adept at acquiring the training data, at labeling it, labeling is an important function there, and who are adept at basically developing and deploying one model after another in an iterative fashion through DevOps, through a standard release pipeline with version controls, and so forth built in, the governance built in. And that's really it needs to be a centralized function, and it's also very compute and data intensive, so you need storage resources, you need large clouds full of high performance computing, and so forth. Be able to handle these functions over and over. Now the edge devices themselves will feed in the data in just the data that is fed into the centralized platform where the training and the modeling is done. So what we're going to see is more and more centralized modeling and training with decentralized execution of the actual inferences that are driven by those models is the way it works in this distributive environment. >> It's the Holy Grail. All right, Jim, we're out of time but thanks very much for helping us unpack and giving us the skinny on machine learning. >> John: It's a fat stack. >> Great to have you in the office and to be continued. Thanks again. >> John: Sure. >> All right, thanks for watching everybody. This is Dave Vellante with Jim Kobelius, and you're watching theCUBE at the Marlboro offices. See ya next time. (upbeat music)

>> Announcer: Live from Barcelona Spain, it's theCUBE! Covering Cisco Live 2020, brought to you by Cisco and its ecosystem partners. >> Welcome back, this is theCUBE's live coverage of Cisco Live 2020 here in Barcelona, Spain. I'm Stu Miniman, my cohost for this segment is Dave Vellante, John Furrier is also in the house. We're doing about three and a half days, wall-to-wall coverage. The surface area that we are covering here is rather broad and I use that term, my guest is laughing, Vikas Butaney, who is the Vice President of IoT, of course. Extending the network to the edge, to the devices, and beyond with Cisco. Thank you so much for joining us. >> It's great to be here. >> All right, the IoT thing. I've worked with Cisco my entire career, I've watched through the fog computing era for a couple of years. Edge of course, one of the hottest conversations, something that I bought up in many of the conversations, the across the portfolio but Liz Centoni was up on the main stage for the day one keynote talking a lot about IoT and IT and OT and your customers of the like. So let's start there, what's new, and how does IoT fit into the overall Cisco Story? >> Absolutely. So as Liz was on the main stage and David talked about the cross domain and multi-domain architecture; Now, IoT and our operational environment is one of the key domains within that environment. And what Liz announce yesterday are two pieces of news that we are releasing at Cisco Live. First of them is an IoT security architecture which ties together the capabilities with cyber vision and then integrates it within the rest of our IT security portfolio and the second part that I'm also excited to talk about is Edge Intelligence. It's about how we are helping our customers extract the data at the edge, then deploy and move it to wherever the applications are in the multicloud environment. >> You know, we definitely want to dig into those pieces, but IoT is such a diverse solution set so it's often helpful to talk about specific industries, any customer examples so what can you share with us there to help illuminate where Cisco's helping the customers love the security angles and edge? >> That's right. Just a level set, when we think about industrial IoT we're really talking about the heavier industries, plant environments for a manufacturing company. We're thinking about roadways for a public sector customer. We're thinking the grid for utility environments. We're thinking refineries and oil extraction upstream environments, right. So this is the kind of spectrum in which we are working in, where customers have real businesses, real assets where the operations is the heart of the enterprise that they are running. And the technology can really be a revolutionary change for them to help them connect and then extract the data and then make sense of the data to improve their business practice so industrial IoT, whether you're a roadway in Austria like Asfinag, you're a utility in Germany like NRG, or EDF in France as an example. Enel in turn in Italy, all of these industries and all of these customers are using industrial IoT technologies in running their businesses better today. >> Where are we in terms of that critical infrastructure being both connected and instrumented? Where are we on the adoption curve? >> Sure, look and many of these industries we have talked about SCADA systems, right, that have been here for thirty plus years for our customers and most of those is really a one-way flow of information, right. And typically customers stood up separate side load networks which weren't really connected to the rest of the enterprise so, Rockwell has a saying from the shop floor to the top floor, right like how the digital enterprise where all of these environments are coming together is where customers are. Critical infrastructure, as you said, in this day and age with security and other kind of threats, customers are a little hesitant about how they connect it all together. But Cisco is working with these customers and helping them think through the benefits they can get but also make sure, from a cyber security point of view, that you're helping protect assets, manage these environments because you can't just arbitrarily connect them because IT tool sets just are not ready to manage these environments. >> I love that all the examples you gave were European, of course, being here in Europe. I'm curious, there's some technologies where North America might take the lead or Asia might take the lead. Is IoT relatively distributed? Is Europe kind of on-par or with the rest of the world when it comes to general adoption? >> What we have found in Europe, because of many countries like Germany leading in the renewable energy effort, and the climate is a big focus here. Data privacy and concerns around data sharing are much more top-of-mind in Europe, so we find those kind of use cases getting adopted much much faster. In Germany, as an example, NRG which is one of our customers, and they were here with us last year at Cisco live and we launched a capability with them. They are trying to manage the real time flow of energy in their grid environment, such that make sure there are no outages, no brownouts in these environments. So utilities and customers like that across Europe are adopting technology faster. Manufacturing, as always, is a leading use case. There we see some of the automotives in US are leading a little bit more in getting environments connected to their environment but overall, IoT is a global market. We work, we have over 70,000 enterprise IoT customers today at Cisco so we are fortunate to be able to serve these customers on a global basis across the range of industries I talked about earlier. >> In a lot of respects too, I would say the US is behind, right, when you look at public policy from a federal standpoint, the US doesn't really have a digital strategy from an overall perspective whereas certainly India does and countries in Europe. You look at the railway systems in Europe. >> Vikas: Much more advanced, yeah. >> Beautiful and shiny and advanced. So I would say the US has a little bit of work to do here, in my perspective. >> That's right, in India Prime Minister Modi started the effort around One Hundred Smart Cities, right, and Cisco is working with many of those smart cities with our Cisco Kinetic for Cities to kind of create, connect all of the sensor networks. Video surveillance, safety, environmental sensors, managing the flow of that data and digitizing those environments, right, and in Europe we've been working in France, Germany, Italy, UK. I think we are seeing much more adoption in these specific industries but it's a global market and again, like I said, 70,000 customers, we get to see quite a bit of the landscape around the globe. >> What should we know about the architecture? Can you give us kind of a high-level summary? What are the basics? >> Sure, so in the comprehensive IoT security architecture we released this week, it really starts with, you have to be able to identify the devices, right. In IT environments, you know, to your laptop and to your PC, they have been managed by MDM technologies for years but in the industrial environment I might have a programmable logic controller that I deployed 15 years ago. It's not ready for modern capabilities so what you really have to start with is identifying all of these assets in the communication baselines that are happening there, that's step one. Step number two is really, now that I know that this is a PLC or that's a controller, I need to come up with a policy, a security policy which says this cell in a plant environment can only talk to the other cell but doesn't need to talk to a paint zone. So I'll give you an example in automotive, if I'm welding a car, I'm building a car, the welding robots need to be communicating with each other. There's no real reason that the welding robot needs to talk to the paint shop, as an example. So you can come up with a set of policies like that to keep these environments separate because if you don't, then if there is one infection, one malware, one security, then it just traverses your whole factory. And we know customers in Europe that their networks have gone down and they've impacted 150 to 200 million dollars of downtime impact. >> Well we had a real world use case 10 years ago or so with Stuxnet with Siemens PLC and boom it went all over the world, I mean it was amazing. >> Exactly right, so again back to identification then I create the policy, then I implement the policy within our switching or a firewall network but you're never done so you have to keep monitoring on a real time basis as the landscape changes. What's happening, how do I keep up with it? And that's where things like anomaly detection are super important, right, so those are the four steps off the architecture that I want to talk about. >> So it sounds like something like cyber security is both a threat and an opportunity of bringing together IT and OT. Bring us inside a little bit those dynamics, we know it's one of the bigger challenges in the IoT space. >> Yeah, I mean I think, look, both parties whether I'm an operational person or an IT person, both of us, both audiences have their own care-abouts. If I'm a plant manager, I'm measured on number of units I'm producing, the quality, the reliability of my products. If I'm in IT I really am measured on downtime of the network or the cyber security threat. There aren't really common measurable capabilities but cyber and security, it kind of brings both the parties together. So when we use our cyber vision product, we're able to provide to that plant manager visibility to what's happening, how are their PLC's performing, did anybody change my program, is my recipe for my given product I'm making secure and safe? So you have to appeal to the operational user with what they care about. IT really cares about to manage the threat surface, don't let that threat kind of propigate. Now at the board level because the board sees both sides of it, they're asking these teams to work together because they have a complimentary skill set. >> Well I think that's critical because, rhetorical question, who's bigger control freaks? Network engineers or operation technology engineers? They both, you know, keep that operation going and are very protective of their infrastructure. So it's got to come from top down and it is a board level discussion, right? >> Yeah that's right, we have customers where, you know, the board, the CEO has mandated to say listen, whether it's for the national threat actors or other corporate espionage, I need to protect the corporate intellectual property. Because it's not just a process, it's also about safety of employees and safety of their assets that comes into play, right. So when some of the customers we're working with, where the CEO has kind of dictated that the IT teams help the operational environments, but it is a two-way street, like, there has to be value for both parties to come together to solve these challenges. >> Okay so we talked a little bit about the threat, also when we're talking IoT, there's all that data involved. What's the opportunity there for customers with data, how's Cisco involved? >> Absolutely, look, I think one of the reasons customers are doing digitization projects is because they're trying to use the data to make better business decisions. It has to improve, yield, and meet their KPI's of their industry. So far what we have seen is that all of the data is really trapped in all of these distributed environments. Gartner tells you that 75% of the data will be produced at the IoT edge. But our customers to date have not had the tool set to be able to get access to the data, cleanse the data at the edge of the network, bring the right data that they can create insights with, and improve their businesses so it's been a heterogeneous environment, lots of protocols, lots of legacy, so that's kind of what our customers are struggling with today. >> Yeah, absolutely and most of that data is going to stay at the edge so I need to be able to process the edge. Heck I even went to a conference last year, talked about satellites that are collecting all of the data, I need to be able to have the storage, the processing, the compute there because I can't send all of the data back, as fast as it is. So it's a changing architecture as to where I collect data, where I process data. We think it is very much additive to traditional cloud and data center environments today, it's just yet another challenge that enterprises need to deal with. >> That's right, so the work that Cisco is doing in the IoT edge environment is we are enabling these customers to connect their remote terminal units, their machines, and their robots and providing them the tool set with four capabilities. First, extract the data. So we have a set of protocols like Modbus, like OPC UA where they can extract the data from their machine so that's step number one. Second is to transform the data, as you said, over an LTE circuit or over a connection, I'm not going to be able to send all of the data back so how do I transform the circuit, transform the data where I maybe take an average over the last five minutes or I kind of put some functions, and we are providing, as we are in the Devnet zone, we are providing developers the capability such that they can use visual studio, they can use Javascript to write logic that can run right at the edge of the network so now you have extracted the data, you have transformed the data. Governance is a key topic, who should have access to my data, especially here in Europe where we're concerned about privacy, we're concerned about data governance. We are enabling our customers to come up with the right logic by which if there's a machine data and you are the supplier, I'm only going to give you the data, the temperature, the vibration, the pressure that you need to support the machine, but I'm not going to give you the number of units I produce. I'm not going to give you the data about my intellectual property. And then you have to integrate to where the data is going, right. So what we're doing is we are working with the public cloud providers, we are working with software ISVs, and we are giving them the integration capability and the benefit of this for the customer is we have done pre-integration on the extraction part and we have done pre-integrations on the delivery part, which allows the projects to go faster and they can deliver their IoT efforts. >> So how do you envision the compute model at the edge, I mean, probably not going to throw a zillion cores so maybe lighter weight components, and I have some follow up on that as well. >> Sure, absolutely. Look, Moore's law is a friend of ours here, right, like with every cycle, every generation of CPU technology, you get more and more compute capabilities. So the IoT gateways that we provide to our customers today have four ARM cores in them. We are using a couple, two of those ARM cores for the networking function but those cores are available for our customers. We have designed an extra memory for them to be able to process these applications and we give them SSD and some storage at that so we can provide up to sixty gigs or one hundred gigs of storage so now that gateway, that communication device, a router, a switch that's at the edge of the network can kind of do a dual purpose. It can not only process and provide you security for the communications but is now an edge processing node so we call them IoT gateways and I can tell you, we are deploying these kind of products on buses. You know, in a mass transit bus, we all ride these buses, there are over six systems that are on that bus. A video surveillance system, I'm going to monitor the tire pressure, I want to monitor if the driver is going over the speed limit. We have now connected all of these systems and we are running logic at the edge such that the riders have a safer experience and then they can get real time visibility to where the bus is as well. >> Yeah and my follow up was on persisting, so you mentioned storage, you know, flash storage at the edge and then you also referred to earlier the challenges this data today is locked in silos or maybe it's not even persisted, it's analog data sometimes. So do you envision, if you think about successful digital companies, kind of born digital, data's at the core and traditionally big manufacturing firms, large infrastructure, the manufacturing plant is the center of the universe and data sort of sits around it. Do you envision a period where that data is somehow virtualized and we have access to it, we could really build digital businesses around that data, what are your thoughts? >> Absolutely. So we have been working with a customer, it's a steel manufacturer in Austria, the heartland of Europe as an example. And they make high quality steel, right, and when they're building the high quality steel, they have two hundred different machine types and like you're saying, the data is trapped in there. This customer is trying to digitize and trying to do that but they have been struggling for the last two years or so to be able to get the data because it's a variety of machines and they want to use our IoT services but they haven't been able to pipeline the data all the way to their cloud environments so that was one of our lighthouse customers and we worked with them like, you know, roll up your sleeves and kind of designed the system with them. And we worked to get that data such that now, they're not quite a born-digital company but they are a hard manufacturing company, they can get the best of the tool sets and analytics and all of the things that contemporary tech companies use and they can bridge them into this digital environment. >> Yeah and this is how the incumbents can compete with the sort of digital natives, right I mean it's an equilibrium that occurs. >> That's right, I mean look we love the digital companies but they're not really, they don't have physical assets there or out there working. They're working in a more physical or more of the real economy whether if you are an oil company and you're getting, extracting oil from a pumpjack, right, well you need to still have the capability to do that better. So that's what we're doing, whether you're a transportation, like the bus example I gave you, an oil and gas company whose trying to extract oil from the ground or you are a manufacturer or you're a utility, if we improve use of our digital technologies and operate, improve the efficiency of the business, a 0.1%, a 1%, that has got a much much bigger implication for us as a society and the world at large. But just making them better and more efficient. >> Huge productivity gains. >> Exactly right, that's right, right. >> Massive, yeah. >> So I think that technology and IoT technologies can benefit all of these industries and you know Cisco is kind of invested and kind of helping our 70,000 customers to get better with all of these capabilities. >> Awesome, congratulations. 70,000 customers, big number, rolling out IoT solutions. Look forward to keeping track of Cisco's IoT solutions. >> Super excited to be here, thanks again. >> For Dave Vellante, I'm Stu Miniman, back with lots more wall-to-wall coverage here at Cisco Live 2020 in Barcelona. Thanks for watching theCUBE. (upbeat music)

[Announcer] Live from Stanford University, it's The Cube! Covering global women in Data Science Conference. Brought to you by SiliconeANGLE Media. >> Welcome back to The Cube. I'm Lisa Martin, live at Stanford University for the fourth annual Women in Date Science Conference, hashtag WiDS2019. We are here with Kavita Sangwan, the Director of Technical Programs, Artificial Intelligence and Machine Learning at Intuit. Kavita, it's wonderful to have you on the program. >> Thank you, pleasure is all mine. >> So Intuit is a global and visionary sponsor of WiDs, and has been for a couple of years. Talk to us a little bit about Intuit's sponsorship of this WiDs movement. >> Sure, well, Tech Women at Intuit has been important part of our culture. It was founded sometime a couple of years back from our previous CTO Taylor Stansbury. He was the founder and sponsor for it, and it has been getting the continuous support and sponsorship from our current CTO, Marianna Tessel. We highly believe that diversity in inclusion, and diversity in talks, and diversity in employees, is an important aspect for our company because that kind of helps us to deliver awesome product experiences and seamless experiences to our customers. This is our second year at WiDs, and we are proud to be part of this event today. >> It's growing tremendously, you know I mentioned it as a movement, and in three and a half years, this is the fourth annual, as I mentioned, and Margot Gerritsen, one of the co founders, chatted with me a couple hours ago and said they're expecting 20,000 people to be engaging today alone. The live stream at the event here at Stanford, but also the impact that they're making. There's a 150 plus regional events going on around this event in 50 plus countries. >> So it's the... You and I were chatting before we went live that you feel this, this palpable energy when you walk in. Tell me a little bit about your role at Intuit, and how you're able to really kind of grow your career in this organization that really seems to support diversity. >> Sure, I head the Technical Program Management for Intuit Data Science Organization, so it's all about data, data science, AI Machine Learning. We apply and imbed AI Machine Learning across all of our product suites. And also try to apply AI Machine Learning in different other aspects as well. Some of the focus areas where we applying AI Machine Learning is making our products smart, security risk and fraud space, where we are all several steps ahead of the fraudsters. Also, in customer success space, and also within the organization, the products and services our work employees use to make their experiences amazing. I have been with Intuit for almost three years now, and it has been an amazing journey. Intuit is such a... It embraces diversity, and it's because of its diverse, durable, innovative culture, I think Intuit has been in Silicone Valley as a strong force for over 35 years. >> So when we think about Data Science, often we think about the technical skills that a data scientist would need to have, right? It's the computational mathematics and engineering, being able to analyze data, but there's this whole other side that seems to be, based on some of the conversations that we've had, as important but maybe lagging behind, and that is skills on being a team player, being collaborative, communication skills, empathy skills. Tell me about, from your perspective, how do you use those skills in your daily job, and how does Intuit maybe foster some of those communication negotiation skills as equal importance as the actual data itself? >> It's very important for us, as we hire our top talent in our organization to empower and grow that top talent as well. We do that by providing them opportunities to learn from different sessions we host around executive presence, negotiation skills, public speaking skills. In addition to advancing them in their technological space. As you rightly said, it's very important for us to operate in a team setting. You know, a data scientist has to interact with a product manager, and a data engineer, a business person, a legal person, because there is questions about security and privacy. So there are so much interactions happening across functional space, it is very important for us to be a team player, and having the ability to have those conversations in the right way. So, Intuit invests heavily, not just in the technology space to advance women, but also in all the other ancillary spaces, which are equally important to be successful as you advance in your career. >> So, as our viewers understand Intuit, I'm a user of it as well for my business, who understand it to a degree. What do you think would surprise our viewers about how Intuit is applying Data Science? >> So, it's important to know that we operate with a customer's mindset. Everything we do starts with our customers, and it's very important for us to build a culture which reflects the values, and the talent, and the skills of our customers. And that is why I said it's very important for us to have diversity in our teams. Our most opportunistic areas for investment in the AI machine learning is the smart products space where we are heavily investing to make our products intelligent, customize it according to the needs of our customers, and giving them great insights for our customers to save them money, make them do less work, and build more confidence in our product suites. >> Confidence, that word kind of reminds me of another word that we hear used a lot around data, and I'm making it very general, but it's trust. That's something that is critical for any business to establish with the customer, but if we look at how much data we're all generating just as people, and how every company has a trail of us with what we eat, what we buy, what we watch, what we download. Where does trust come into play, if you're really designing these things for the customer in mind, how are you delivering on that promise of trust? >> It's very rightly said, just to add to that sentiment, it has been shared in some articles that we have accumulated so much data in the last two years which is more than what we have accumulated in the last five thousand years of humanity. It is really important to have trust with your customers because we are using their data for their own benefits. Intuit operates with the principle and the mindset that this our customer's data, and we are their stewards. We make sure that we are one of the best stewards for their data, and that's what we reflect in our products, how we serve them, build intelligent products for them, and that's how we start to gain trust from our customers. >> And I imagine being quite transparent in the process. >> That's true, yes. >> So in terms of your career, I was doing some research on you, and I know that you love to give back to the community by being a champion for women in technology, encouraging young girls in STEM towards building that community. Tell me a little bit about your career as we are here at WiDS at Stanford there's a lot of involvement in the student community. Tell me a little about your background and what some of your favorite things are about giving back to the next generation. >> Sure, I actually, when I graduated from engineering, I was one of the four women students out of the, maybe, a class of around 50 students. So I think it struck me right there that there is a disparity in the industry, in the education system, and then in the industry. I felt the same thing in my different companies where I worked, and that always led me to a point that I actually, rather than just being observing this from afar, why can't I be the one who moved the needle on this? That led me to a point where I started collaborating within the companies, started forming teams, and started working with the teams who were already there to move the needle in technical women's space. I think, if I reflect back in my journey, a couple of things that stand out for me is passion for what you do, and I am really passionate about what my goal is and I try to line up my work according to that and that's why this women in tech, something which is close to my heart and I'm passionate about, always comes forward whenever I do something. The second important aspect is, I've always thrown myself into situations which I've never done before. For example we were offline talking about hackathon, which is DevelopHer. I had never done any hackathons before because I was so passionate about doing it, I just threw myself in and I ran that hackathon. And then the third thing is being persistent about what you do. I mean, you can't just do one thing and then drop it and then come back after a few weeks and then do it again. You have to have that consistency of doing it, only then do you start moving the needle. I think when I reflect and look back, these three things stand out for me and that has applied in my own personal career, as well as everything I do in my life. >> How do you give, and the last question, it seems like you sort of have that natural passion, I love this, this is what I want to do, you were persistent with it, how do you advise younger girls who might not have that natural passion to really develop that within themselves? >> I think experiment and explore. When you try to do different things, only then you find out where your passion lies. Just don't be scared of throwing yourself into a situation which you have never dealt before. Always try to find new things and throw yourself in an uncomfortable situation, and try to get out of it. It helps you become super bold, and gives you confidence, and that's the way to find what you're naturally passionate about. >> I like that, I like to say get comfortably uncomfortable. Last question in the last few seconds, I just want you to have the opportunity to tell our viewers where they can go to learn more about Intuit and their Data Science jobs. >> Yes, you can always go to intuit.com, and intuitcareers.com, and learn about the great opportunities we have for Intuit and Data Science. >> Excellent, well Kavita, it's been a pleasure to have you on The Cube this afternoon. Thank you for stopping by, and also for sharing what Intuit is doing to support WiDS. >> Thank you, it was my pleasure, thank you so much. >> We want to thank you for watching The Cube, I'm Lisa Martin live from the WiDS fourth annual WiDS global conference at Stanford. Stick around, I'll be right back with our next guest.