(upbeat music) >> Okay, welcome back everyone at Supercloud 2 event, live here in Palo Alto, theCUBE Studios live stage performance, virtually syndicating it all over the world. I'm John Furrier with Dave Vellante here as Cube alumni, and special influencer guest, Howie Xu, VP of Machine Learning and Zscaler, also part-time as a CUBE analyst 'cause he is that good. Comes on all the time. You're basically a CUBE analyst as well. Thanks for coming on. >> Thanks for inviting me. >> John: Technically, you're not really a CUBE analyst, but you're kind of like a CUBE analyst. >> Happy New Year to everyone. >> Dave: Great to see you. >> Great to see you, Dave and John. >> John: We've been talking about ChatGPT online. You wrote a great post about it being more like Amazon, not like Google. >> Howie: More than just Google Search. >> More than Google Search. Oh, it's going to compete with Google Search, which it kind of does a little bit, but more its infrastructure. So a clever point, good segue into this conversation, because this is kind of the beginning of these kinds of next gen things we're going to see. Things where it's like an obvious next gen, it's getting real. Kind of like seeing the browser for the first time, Mosaic browser. Whoa, this internet thing's real. I think this is that moment and Supercloud like enablement is coming. So this has been a big part of the Supercloud kind of theme. >> Yeah, you talk about Supercloud, you talk about, you know, AI, ChatGPT. I really think the ChatGPT is really another Netscape moment, the browser moment. Because if you think about internet technology, right? It was brewing for 20 years before early 90s. Not until you had a, you know, browser, people realize, "Wow, this is how wonderful this technology could do." Right? You know, all the wonderful things. Then you have Yahoo and Amazon. I think we have brewing, you know, the AI technology for, you know, quite some time. Even then, you know, neural networks, deep learning. But not until ChatGPT came along, people realize, "Wow, you know, the user interface, user experience could be that great," right? So I really think, you know, if you look at the last 30 years, there is a browser moment, there is iPhone moment. I think ChatGPT moment is as big as those. >> Dave: What do you see as the intersection of things like ChatGPT and the Supercloud? Of course, the media's going to focus, journalists are going to focus on all the negatives and the privacy. Okay. You know we're going to get by that, right? Always do. Where do you see the Supercloud and sort of the distributed data fitting in with ChatGPT? Does it use that as a data source? What's the link? >> Howie: I think there are number of use cases. One of the use cases, we talked about why we even have Supercloud because of the complexity, because of the, you know, heterogeneous nature of different clouds. In order for me as a developer, in order for me to create applications, I have so many things to worry about, right? It's a complexity. But with ChatGPT, with the AI, I don't have to worry about it, right? Those kind of details will be taken care of by, you know, the underlying layer. So we have been talking about on this show, you know, over the last, what, year or so about the Supercloud, hey, defining that, you know, API layer spanning across, you know, multiple clouds. I think that will be happening. However, for a lot of the things, that will be more hidden, right? A lot of that will be automated by the bots. You know, we were just talking about it right before the show. One of the profound statement I heard from Adrian Cockcroft about 10 years ago was, "Hey Howie, you know, at Netflix, right? You know, IT is just one API call away." That's a profound statement I heard about a decade ago. I think next decade, right? You know, the IT is just one English language away, right? So when it's one English language away, it's no longer as important, API this, API that. You still need API just like hardware, right? You still need all of those things. That's going to be more hidden. The high level thing will be more, you know, English language or the language, right? Any language for that matter. >> Dave: And so through language, you'll tap services that live across the Supercloud, is what you're saying? >> Howie: You just tell what you want, what you desire, right? You know, the bots will help you to figure out where the complexity is, right? You know, like you said, a lot of criticism about, "Hey, ChatGPT doesn't do this, doesn't do that." But if you think about how to break things down, right? For instance, right, you know, ChatGPT doesn't have Microsoft stock price today, obviously, right? However, you can ask ChatGPT to write a program for you, retrieve the Microsoft stock price, (laughs) and then just run it, right? >> Dave: Yeah. >> So the thing to think about- >> John: It's only going to get better. It's only going to get better. >> The thing people kind of unfairly criticize ChatGPT is it doesn't do this. But can you not break down humans' task into smaller things and get complex things to be done by the ChatGPT? I think we are there already, you know- >> John: That to me is the real game changer. That's the assembly of atomic elements at the top of the stack, whether the interface is voice or some programmatic gesture based thing, you know, wave your hand or- >> Howie: One of the analogy I used in my blog was, you know, each person, each professional now is a quarterback. And we suddenly have, you know, a lot more linebacks or you know, any backs to work for you, right? For free even, right? You know, and then that's sort of, you should think about it. You are the quarterback of your day-to-day job, right? Your job is not to do everything manually yourself. >> Dave: You call the play- >> Yes. >> Dave: And they execute. Do your job. >> Yes, exactly. >> Yeah, all the players are there. All the elves are in the North Pole making the toys, Dave, as we say. But this is the thing, I want to get your point. This change is going to require a new kind of infrastructure software relationship, a new kind of operating runtime, a new kind of assembler, a new kind of loader link things. This very operating systems kind of concepts. >> Data intensive, right? How to process the data, how to, you know, process so gigantic data in parallel, right? That's actually a tough job, right? So if you think about ChatGPT, why OpenAI is ahead of the game, right? You know, Google may not want to acknowledge it, right? It's not necessarily they do, you know, not have enough data scientist, but the software engineering pieces, you know, behind it, right? To train the model, to actually do all those things in parallel, to do all those things in a cost effective way. So I think, you know, a lot of those still- >> Let me ask you a question. Let me ask you a question because we've had this conversation privately, but I want to do it while we're on stage here. Where are all the alpha geeks and developers and creators and entrepreneurs going to gravitate to? You know, in every wave, you see it in crypto, all the alphas went into crypto. Now I think with ChatGPT, you're going to start to see, like, "Wow, it's that moment." A lot of people are going to, you know, scrum and do startups. CTOs will invent stuff. There's a lot of invention, a lot of computer science and customer requirements to figure out. That's new. Where are the alpha entrepreneurs going to go to? What do you think they're going to gravitate to? If you could point to the next layer to enable this super environment, super app environment, Supercloud. 'Cause there's a lot to do to enable what you just said. >> Howie: Right. You know, if you think about using internet as the analogy, right? You know, in the early 90s, internet came along, browser came along. You had two kind of companies, right? One is Amazon, the other one is walmart.com. And then there were company, like maybe GE or whatnot, right? Really didn't take advantage of internet that much. I think, you know, for entrepreneurs, suddenly created the Yahoo, Amazon of the ChatGPT native era. That's what we should be all excited about. But for most of the Fortune 500 companies, your job is to surviving sort of the big revolution. So you at least need to do your walmart.com sooner than later, right? (laughs) So not be like GE, right? You know, hand waving, hey, I do a lot of the internet, but you know, when you look back last 20, 30 years, what did they do much with leveraging the- >> So you think they're going to jump in, they're going to build service companies or SaaS tech companies or Supercloud companies? >> Howie: Okay, so there are two type of opportunities from that perspective. One is, you know, the OpenAI ish kind of the companies, I think the OpenAI, the game is still open, right? You know, it's really Close AI today. (laughs) >> John: There's room for competition, you mean? >> There's room for competition, right. You know, you can still spend you know, 50, $100 million to build something interesting. You know, there are company like Cohere and so on and so on. There are a bunch of companies, I think there is that. And then there are companies who's going to leverage those sort of the new AI primitives. I think, you know, we have been talking about AI forever, but finally, finally, it's no longer just good, but also super useful. I think, you know, the time is now. >> John: And if you have the cloud behind you, what do you make the Amazon do differently? 'Cause Amazon Web Services is only going to grow with this. It's not going to get smaller. There's more horsepower to handle, there's more needs. >> Howie: Well, Microsoft already showed what's the future, right? You know, you know, yes, there is a kind of the container, you know, the serverless that will continue to grow. But the future is really not about- >> John: Microsoft's shown the future? >> Well, showing that, you know, working with OpenAI, right? >> Oh okay. >> They already said that, you know, we are going to have ChatGPT service. >> $10 billion, I think they're putting it. >> $10 billion putting, and also open up the Open API services, right? You know, I actually made a prediction that Microsoft future hinges on OpenAI. I think, you know- >> John: They believe that $10 billion bet. >> Dave: Yeah. $10 billion bet. So I want to ask you a question. It's somewhat academic, but it's relevant. For a number of years, it looked like having first mover advantage wasn't an advantage. PCs, spreadsheets, the browser, right? Social media, Friendster, right? Mobile. Apple wasn't first to mobile. But that's somewhat changed. The cloud, AWS was first. You could debate whether or not, but AWS okay, they have first mover advantage. Crypto, Bitcoin, first mover advantage. Do you think OpenAI will have first mover advantage? >> It certainly has its advantage today. I think it's year two. I mean, I think the game is still out there, right? You know, we're still in the first inning, early inning of the game. So I don't think that the game is over for the rest of the players, whether the big players or the OpenAI kind of the, sort of competitors. So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest, to get, you know, another shot to the OpenAI sort of the level?" You know, I did a- (laughs) >> Line up. >> That's classic VC. "How much does it cost me to replicate?" >> I'm pretty sure he asked the question to a bunch of guys, right? >> Good luck with that. (laughs) >> So we kind of did some napkin- >> What'd you come up with? (laughs) >> $100 million is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So 100 million. >> John: Hundreds of millions. >> Yeah, yeah, yeah. 100 million order of magnitude is what I came up with. You know, we can get into details, you know, in other sort of the time, but- >> Dave: That's actually not that much if you think about it. >> Howie: Exactly. So when he heard me articulating why is that, you know, he's thinking, right? You know, he actually, you know, asked me, "Hey, you know, there's this company. Do you happen to know this company? Can I reach out?" You know, those things. So I truly believe it's not a billion or 10 billion issue, it's more like 100. >> John: And also, your other point about referencing the internet revolution as a good comparable. The other thing there is online user population was a big driver of the growth of that. So what's the equivalent here for online user population for AI? Is it more apps, more users? I mean, we're still early on, it's first inning. >> Yeah. We're kind of the, you know- >> What's the key metric for success of this sector? Do you have a read on that? >> I think the, you know, the number of users is a good metrics, but I think it's going to be a lot of people are going to use AI services without even knowing they're using it, right? You know, I think a lot of the applications are being already built on top of OpenAI, and then they are kind of, you know, help people to do marketing, legal documents, you know, so they're already inherently OpenAI kind of the users already. So I think yeah. >> Well, Howie, we've got to wrap, but I really appreciate you coming on. I want to give you a last minute to wrap up here. In your experience, and you've seen many waves of innovation. You've even had your hands in a lot of the big waves past three inflection points. And obviously, machine learning you're doing now, you're deep end. Why is this Supercloud movement, this wave of Supercloud and the discussion of this next inflection point, why is it so important? For the folks watching, why should they be paying attention to this particular moment in time? Could you share your super clip on Supercloud? >> Howie: Right. So this is simple from my point of view. So why do you even have cloud to begin with, right? IT is too complex, too complex to operate or too expensive. So there's a newer model. There is a better model, right? Let someone else operate it, there is elasticity out of it, right? That's great. Until you have multiple vendors, right? Many vendors even, you know, we're talking about kind of how to make multiple vendors look like the same, but frankly speaking, even one vendor has, you know, thousand services. Now it's kind of getting, what Kid was talking about what, cloud chaos, right? It's the evolution. You know, the history repeats itself, right? You know, you have, you know, next great things and then too many great things, and then people need to sort of abstract this out. So it's almost that you must do this. But I think how to abstract this out is something that at this time, AI is going to help a lot, right? You know, like I mentioned, right? A lot of the abstraction, you don't have to think about API anymore. I bet 10 years from now, you know, IT is one language away, not API away. So think about that world, right? So Supercloud in, in my opinion, sure, you kind of abstract things out. You have, you know, consistent layers. But who's going to do that? Is that like we all agreed upon the model, agreed upon those APIs? Not necessary. There are certain, you know, truth in that, but there are other truths, let bots take care of, right? Whether you know, I want some X happens, whether it's going to be done by Azure, by AWS, by GCP, bots will figure out at a given time with certain contacts with your security requirement, posture requirement. I'll think that out. >> John: That's awesome. And you know, Dave, you and I have been talking about this. We think scale is the new ratification. If you have first mover advantage, I'll see the benefit, but scale is a huge thing. OpenAI, AWS. >> Howie: Yeah. Every day, we are using OpenAI. Today, we are labeling data for them. So you know, that's a little bit of the- (laughs) >> John: Yeah. >> First mover advantage that other people don't have, right? So it's kind of scary. So I'm very sure that Google is a little bit- (laughs) >> When we do our super AI event, you're definitely going to be keynoting. (laughs) >> Howie: I think, you know, we're talking about Supercloud, you know, before long, we are going to talk about super intelligent cloud. (laughs) >> I'm super excited, Howie, about this. Thanks for coming on. Great to see you, Howie Xu. Always a great analyst for us contributing to the community. VP of Machine Learning and Zscaler, industry legend and friend of theCUBE. Thanks for coming on and sharing really, really great advice and insight into what this next wave means. This Supercloud is the next wave. "If you're not on it, you're driftwood," says Pat Gelsinger. So you're going to see a lot more discussion. We'll be back more here live in Palo Alto after this short break. >> Thank you. (upbeat music)

(bright music) >> Hey, welcome to this "CUBE Conversation." I'm your host, Lisa Martin. I've got two guests from NETSCOUT here with me today. Eileen Haggerty joins us, the AVP of Product and Solutions Marketing and Jason Chaffee, Senior Product Manager. We're going to be talking about the importance of quality end user experience with UC&C, Unified Communications and Collaborations services for something that will be near and dear to all of our hearts, employee productivity. Eileen, let's go ahead and start with you and the impact of COVID on UC&C, what has it been? >> Oh, Lisa, great question, because we really have seen an evolution in the importance and reliance on UCC. COVID would not have allowed us to go to work, do business continuity, any of those things had it not been for strong communications platforms to help us do that. And in fact, really the hero of all of this has been what's called Unified Communications as a Service or UCaaS. Enterprise businesses really depended entirely on the communications between the home office and the employees remotely. This is also known to be the way we all went to work. It was no longer a car. We picked up the phone basically or the computer. So Zoom, WebEx, Teams, Google Meets, they've all become household names really over the last two years. That's kind of exciting for them. And businesses during that period of time expanded their tools to keep business running and employees in communication using these very platforms, and we'll refer to this a couple of times during this conversation too, Lisa. We did a survey at the end of 2021, IT leaders, about their use of UCaaS and UCC during this period of time. We found that almost of them had used collaboration tools, and in fact, added to their arsenal of tools during this period of time to such an extent that they're now ranging, the majority of them, between three and nine different platforms that their corporate employees use. This became unwieldy, of course, during that time, and so their strategy going forward is going to be to reduce some of that number, but pretty interesting details. >> Yeah, between three and nine is a lot, and certainly, UC&C became a lifeline for all of us, professionally and personally. Even my mom learned how to use Zoom during this time. I was pretty proud of helping her with that. But, Jason, talk to us about all these new communication services. We're completely dependent on them, but overall what have you found out in terms of how they worked out? >> Well, to be honest with you, I think from the IT organization, it's been a challenge. It's been difficult. I think every IT organization is really motivated to ensure the quality of the services throughout the whole company, but as you can imagine, the increase of these communications Eileen just talked about during the pandemic is just significantly increasing the number of IT help desk tickets that have come through. And in that survey that Eileen just talked about, in fact, a third of those that responded said that 50 to 75% of their help desk tickets right now are related to UC&C or UCaaS services, and in fact, they say that over half of them have said that they get those tickets at least once a day, if not multiple times a day. And I think another big aspect of this that's been a challenge is everybody working from home now and the whole hybrid environment, and IT teams are really trying to understand and make sure that they get the same delivery of services if they were in the corporate headquarters, and I think they felt a loss of control and visibility in the services that are being delivered. I think the other thing that came out of this survey was about 25% of those said that they could get these issues if and when they happen, resolved in just a matter of minutes, but most said that it can take hours or even days to get through those, and that's obviously a really bad look for the company and really hinders productivity. So overall, I'd say it's been a challenge. I think as this onslaught of services that have come through and hampered and that everyone's trying to manage and get through, along with the lack of visibility when everybody's working from home. Of course, it's been fantastic for those of us that are working from home and made everything easier, but I think it's just made it that much more difficult for the IT teams that are trying to manage this new environment. >> Right, definitely difficulty behind the scenes there. You talked about the 25% of IT organizations being able to resolve quickly, but that leaves 75% of organizations where it takes more than a few minutes, and I can imagine individually, that might not be a big impact, but, Eileen, overall if it's taking more than a few minutes to resolve UC&C IT help desk issues, what's the overall impact to a business? >> It can be significant, and we hear a lot of little stories sadly sometimes on Lester Holt's evening news, but really what you are looking at here are longer periods of time where employees can't talk to each other. We've got email. We can probably compensate a different manner, but when it happens to be your customers not being able to talk to customer service reps in the contact center, couple of hours, that can be a big issue. Partners and suppliers who might be trying to get you important information very quickly. Maybe it's a supply chain issue item that they want to alert you to that you need to act on. That's a long period of time. And I think it's kind of important here to call out one special group, and that would be corporate executives. I think we've all heard about these big town hall meetings that corporate executives may be holding with employees or investors and all of a sudden their UCaaS support freezes, or it doesn't connect the voice in the video, and all of a sudden, you've got a very embarrassing situation. It really gets the attention of the public. Losing communication for a couple of hours, bottom line, it is going to impact productivity, customer service, and it could impact reputation, especially with those social media influencers that we all both favor and fear. So, when we were talking about our survey results, that is actually a top concern of IT executives, that productivity will get hit if communications problems do exist. So I think really ultimately for all of us in the business, disruptions and communication, it's going to be bad for business, any length. >> It is bad for business at any length, and that's a huge risk for businesses in any industry. I've been on those executive town halls where video wouldn't connect, and you just think, as much as we wanted that human connection during this time, and you couldn't get it, it made the the interaction not as ideal and obviously a risk for the organization. So, Jason, how can IT then jump in and resolve these disruptions faster, because time is of the essence here? >> Well, yeah, exactly. As we've discussed and Eileen just talked about, I think resolving issues quickly is really the key. I think we all know issues are going to happen, they just will, but it's really the IT team that can solve those the fastest is the team that's going to win, and so I think that's really the key to all of that. And one of the things that comes out of that is, again, from this survey is that only about 54% of the respondents said that they felt confident that they could understand root cause and be able to get to those issues quickly, which leaves about 43%, almost as many, that said they were less than confident or somewhat confident in finding that root cause, and so I think that's really the key there is really having the confidence to be able to find that, and to get that confidence, you need to be able to understand root cause quickly. And in order to get that, I think you need a combination of two things, which is passive, packet-based monitoring as well as continuous active testing or monitoring of those solutions. So, what I mean by that is being able to automatically and continuously test these services, even if nobody's on the system and nobody's on trying to make a phone call. So you have somebody who's trying to host, an active agent that's trying to host a meeting and others that are trying to join the meeting and sending an audio and sending and receiving video and looking at the measurements and trying to take all of that data in to really proactively understand what's going on and doing this every 15 minutes or every once an hour to really, again, get ahead of things before they become a problem. But I think beyond that, it's really about being able to take that data and the packets from those transactions that you were just testing and be able to trend that data and define problems and diagnose issues proactively. Again, as Eileen just said, before the CEO gets on there and tries to make his town hall call, so that that's really important to be able to solve those things more quickly. I think it's really a combination of a passive, scalable monitoring solution along with scheduled automatic testing of those, and along with the packets that go with that, that's really a combination of both. It's kind of a best of both worlds in order to get those things solved quickly. >> To get them solved quickly, I want to go back to something that Eileen said. You mentioned the word 'confidence,' and that I think it's important to point out that you're not saying that trivially, that IT needs to have the confidence that it has the right solutions in place to discover these faster. Eileen, from your perspective, talk to me about what that confidence means to IT and how it can shift up the stack to the C-suite. >> You know, honestly, processes and policies in these organizations are critical. They need to be able to notice when the trouble ticket comes in, and there's a lot of 'em, let's face it, and they're coming from all kinds of locations. Now, it's some of the remote offices. Some of 'em are still people at home. You've got to be able to know where to turn, what screen to use, what tool to adjust, what workflow to process, and that does come with practice, but it also comes with a solid set of tools and visibility strategies, and then you follow that process through, you work together. Maybe the voice, people in the network, people have to work together, maybe the cloud people, 'cause it's a contract with UCaaS, work together, gather the evidence and pinpoint the solution that's going to fix the problem with those locations. And it is, it becomes then a confidence builder, proof points. >> Right, proof points are critical. So, the solution that you both talked about, Jason, you elaborated on this, I'd love to get some real world examples. Tell me how you've seen this in practice. Jason, we'll start with you and then, Eileen, we'll go to you. >> Okay, yeah, great, I was just thinking of one that we had that really was one of the largest insurance companies in the country, if not the world, and when the pandemic hit, they suddenly had to send everybody home, and this is the lifeblood of their company, the contact centers that are answering these calls and the ones that were processing these claims. And as everybody went home, their strategy really was to actually go buy new laptops for everyone and implement VPNs that had a little bit of, but not fully and then implement SD-WAN, and so they had all of this traffic going over VPNs and through SD-WANs and new UCaaS solutions and all of this and what they quickly learned and found out was they just didn't have the visibility to be able to fuel, again, that word confidence that they were serving their their customers very well. So, they actually implemented one of our solutions and put these agents out at all their different desktops and started watching and doing these proactive calls and making going through the meeting life cycle and actually testing the bandwidths of their SD-WAN and ensuring all of those services. And what they found was they were able to solve some of the solutions that are even harder to solve normally, because it was affecting some users, but not all of them, and that's often harder to try and get their arms around. And so as they continued to do this, and just got their arms more around it and got more visibility, they really feel like everything's under control. And as of now, they're actually planning on leaving all those users working from home now, because they can actually ensure the same type of experience for both the users and for their customers as if those people were working from their corporate headquarters. >> Jason, that sort of sounds like a bit of a COVID silver lining. >> (laughs) Yeah, I think so. I think a lot of us actually started working from home and so there was kind of the silver lining of flexibility for the employee, but for the customer and the company itself, they learned this new visibility and this new way to ensure that across everywhere, wherever they may be, and I don't know that that would've come out without the COVID silver lining, as you just said. So I think it was something that really came out of it that might've been a good thing. >> And there are a few of those, which is nice. Eileen, talk to me about some of the experiences that you've had. What have you seen out in the field? >> Yeah, we have one really terrific energy company that was talking with us the other day, and their employees use Microsoft Office 365 which has the teams collaboration and communications system with it. And, what they've been doing for those at-home employees was configuring tests on their works stations, much like Jason explained, but it mimics exactly how an employee might be making their call and joining the sessions from video to audio, to going through login and log out. What's interesting is, and this is a compelling differentiator, a lot of tools may just watch traffic as it's happening, and certainly that's a value, but these tests even run when our agents are asleep. And what that does is these are all 24 hour a day businesses, and so maybe they have followed-the-sun contact centers or whatnot and something's happening in one part of the world, but then it's rolling to others, and we have all heard those disaster stories online when we wake up and we're hearing it on the morning news. So, if an organization can find the problem and detect it early enough and then get it when it's a few people that are involved, they can actually resolve it with our tools, find the root cause, implement a corrective action before the majority of their agents are even logging in in the morning. Nobody even knew that there was a problem overnight, because they were able to get to it and resolve it faster, and when you can do that, you're being proactive. And this, again, builds on the confidence that you get doing this kind of activity over and over and over again. But at the same time, it's also enormously beneficial from a business productivity perspective for the employees and certainly reputationally in revenue-based customer service, making sure that things are available whenever they're necessary. So, making sure they can perform their jobs, I know it sounds trite, but it's really the most critical thing we can help 'em with. >> Absolutely, 'cause I think, Eileen, one of the things that I've always thought for years is that employee productivity and employee satisfaction is directly tied to customer satisfaction, customer delight, and as you talked about, there's plenty of social media influencers who are happy to share news, good or bad, so that employee productivity is a direct relation on the customer satisfaction, the brand reputation. Jason, what are your thoughts there? >> Well, I think that's exactly right. I think it's, again, being able to continuously have your arms around that and make sure, because if you can't make phone calls or customers can't call in or things aren't working then it is, it's really a revenue impact, but it's also reputation impact, and you're going to remember that company that just didn't have their act together if you will, so I think it's important to, again, invest in this and make sure that no matter what, wherever your end users are or wherever your employees are, you're providing that experience just as if they were in the corporate office, and even when they're in the corporate office, being able to, as Eileen talked about, know ahead of time and proactively when issues happen in these very complex UCaaS and UC&C solutions that are out there now. >> And last question, Eileen for you, I imagine that these solutions are horizontal across every industry, every type of business, every size of business? >> Yeah, it's one of those phenomenon that's really critical is the ability to be ubiquitous in any environment, not being vendor-specific or dependent because now look at it, we shot that stat, three to nine different platforms in one company. If you had to buy three or nine different platforms to resolve problems, that reduces your ability to build workflows, consistent ones and know what you're doing every single time. You'd have to learn nine different platforms. That's not productive and that's certainly not realistic. So yeah, I think that this is really key. You have to be able to look at all of the traffic and be able to resolve the problems, regardless of what they happen to be running on. >> And the great thing is hearing the tools and the capabilities and solutions that NETSCOUT has to help businesses in any industry, at any size be able to identify these issues, resolve them faster and then create some silver linings. Guys, thank you so much for joining me today. Always a pleasure talking to you. This was really interesting to talk about the importance of quality end user experience with communication services for the employee productivity and of course, ultimately consumer customer satisfaction. We appreciate your insights. >> Thank you so much. >> Thank you. >> For Eileen Haggerty and Jason Chaffee, I'm Lisa Martin, you're watching a "CUBE Conversation." (bright music)

(upbeat electronic music) >> Narrator: From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE. Covering when IoT met AI, The Intelligence of Things. Brought to you by Western Digital. >> Welcome back here everybody, Jeff Frick here with theCUBE. We are at downtown San Jose at the Fairmont Hotel. When IoT met AI it happened right here, you saw it first. The Intelligence of Things, a really interesting event put on by readwrite and Western Digital and we are really excited to welcome back a many time CUBE alumni and always a fan favorite, she's Janet George. She's Fellow & Chief Data Officer of Western Digital. Janet, great to see you. >> Thank you, thank you. >> So, as I asked you when you sat down, you're always working on cool things. You're always kind of at the cutting edge. So, what have you been playing with lately? >> Lately I have been working on neural networks and TensorFlow. So really trying to study and understand the behaviors and patterns of neural networks, how they work and then unleashing our data at it. So trying to figure out how it's training through our data, how many nets there are, and then trying to figure out what results it's coming with. What are the predictions? Looking at how the predictions are, whether the predictions are accurate or less accurate and then validating the predictions to make it more accurate, and so on and so forth. >> So it's interesting. It's a different tool, so you're learning the tool itself. >> Yes. >> And you're learning the underlying technology behind the tool. >> Yes. >> And then testing it actually against some of the other tools that you guys have, I mean obviously you guys have been doing- >> That's right. >> Mean time between failure analysis for a long long time. >> That's right, that's right. >> So, first off, kind of experience with the tool, how is it different? >> So with machine learning, fundamentally we have to go into feature extraction. So you have to figure out all the features and then you use the features for predictions. With neural networks you can throw all the raw data at it. It's in fact data-agnostic. So you don't have to spend enormous amounts of time trying to detect the features. Like for example, If you throw hundreds of cat images at the neural network, the neural network will figure out image features of the cat; the nose, the eyes, the ears and so on and so forth. And once it trains itself through a series of iterations, you can throw a lot of deranged cats at the neural network and it's still going to figure out what the features of a real cat is. >> Right. >> And it will predict the cat correctly. >> Right. So then, how does that apply to, you know, the more specific use case in terms of your failure analysis? >> Yeah. So we have failures and we have multiple failures. Some failures through through the human eye, it's very obvious, right? But humans get tired, and over a period of time we can't endure looking at hundreds and millions of failures, right? And some failures are interconnected. So there is a relationship between these failure patterns or there is a correlation between two failures, right? It could be an edge failure. It could a radial failure, eye pattern type failure. It could be a radial failure. So these failures, for us as humans, we can't escape. >> Right. >> And we used to be able to take these failures and train them at scale and then predict. Now with neural networks, we don't have to take and do all that. We don't have to extract these labels and try to show them what these failures look like. Training is almost like throwing a lot of data at the neural networks. >> So it almost sounds like kind of the promise of the data lake if you will. >> Yes. >> If you have heard about, from the Hadoop Summit- >> Yes, yes, yes. >> For ever and ever and ever. Right? You dump it all in and insights will flow. But we found, often, that that's not true. You need hypothesis. >> Yes, yes. >> You need to structure and get it going. But what you're describing though, sounds much more along kind of that vision. >> Yes, very much so. Now, the only caveat is you need some labels, right? If there is no label on the failure data, it's very difficult for the neural networks to figure out what the failure is. >> Jeff: Right. >> So you have to give it some labels to understand what patterns it should learn. >> Right. >> Right, and that is where the domain experts come in. So we train it with labeled data. So if you are training with a cat, you know the features of a cat, right? In the industrial world, cat is really what's in the heads of people. The domain knowledge is not so authoritative. Like the sky or the animals or the cat. >> Jeff: Right. >> The domain knowledge is much more embedded in the brains of the people who are working. And so we have to extract that domain knowledge into labels. And then you're able to scale the domain. >> Jeff: Right. >> Through the neural network. >> So okay so then how does it then compare with the other tools that you've used in the past? In terms of, obviously the process is very different, but in terms of just pure performance? What are you finding? >> So we are finding very good performance and actually we are finding very good accuracy. Right? So once it's trained, and it's doing very well on the failure patterns, it's getting it right 90% of the time, right? >> Really? >> Yes, but in a machine learning program, what happens is sometimes the model is over-fitted or it's under-fitted or there is bias in the model and you got to remove the bias in the model or you got to figure out, well, is the model false-positive or false-negative? You got to optimize for something, right? >> Right, right. >> Because we are really dealing with mathematical approximation, we are not dealing with preciseness, we are not dealing with exactness. >> Right, right. >> In neural networks, actually, it's pretty good, because it's actually always dealing with accuracy. It's not dealing with precision, right? So it's accurate most of the time. >> Interesting, because that's often what's common about the kind of difference between computer science and statistics, right? >> Yes. >> Computers is binary. Statistics always has a kind of a confidence interval. But what you're describing, it sounds like the confidence is tightening up to such a degree that it's almost reaching binary. >> Yeah, yeah, exactly. And see, brute force is good when your traditional computing programing paradigm is very brute force type paradigm, right? The traditional paradigm is very good when the problems are simpler. But when the problems are of scale, like you're talking 70 petabytes of data or you're talking 70 billion roles, right? Find all these patterns in that, right? >> Jeff: Right. >> I mean you just, the scale at which that operates and at the scale at which traditional machine learning even works is quite different from how neural networks work. >> Jeff: Okay. >> Right? Traditional machine learning you still have to do some feature extraction. You still have to say "Oh I can't." Otherwise you are going to have dimensionality issues, right? It's too broad to get the prediction anywhere close. >> Right. >> Right? And so you want to reduce the dimensionality to get a better prediction. But here you don't have to worry about dimensionality. You just have to make sure the labels are right. >> Right, right. So as you dig deeper into this tool and expose all these new capabilities, what do you look forward to? What can you do that you couldn't do before? >> It's interesting because it's grossly underestimating the human brain, right? The human brain is supremely powerful in all aspects, right? And there is a great deal of difficulty in trying to code the human brain, right? But with neural networks and because of the various propagation layers and the ability to move through these networks we are coming closer and closer, right? So one example: When you think about driving, recently, Google driverless car got into an accident, right? And where it got into an accident was the driverless car was merging into a lane and there was a bus and it collided with the bus. So where did A.I. go wrong? Now if you train an A.I., birds can fly, and then you say penguin is a bird, it is going to assume penguin can fly. >> Jeff: Right, right. >> We as humans know penguin is a bird but it can't fly like other birds, right? >> Jeff: Right. >> It's that anomaly thing, right? Naturally when are driving and a bus shows up, even if it's yield, the bus goes. >> Jeff: Right, right. >> We yield to the bus because it's bigger and we know that. >> A.I. doesn't know that. It was taught that yield is yield. >> Right, right. >> So it collided with the bus. But the beauty is now large fleets of cars can learn very quickly based on what it just got from that one car. >> Right, right. >> So now there are pros and cons. So think about you driving down Highway 85 and there is a collision, it's Sunday morning, you don't know about the collision. You're coming down on the hill, right? Blind corner and boom that's how these crashes happen and so many people died, right? If you were driving a driverless car, you would have knowledge from the fleet and from everywhere else. >> Right. >> So you know ahead of time. We don't talk to each other when we are in cars. We don't have universal knowledge, right? >> Car-to-car communication. >> Car-to-car communications and A.I. has that so directly it can save accidents. It can save people from dying, right? But people still feel, it's a psychology thing, people still feel very unsafe in a driverless car, right? So we have to get over- >> Well they will get over that. They feel plenty safe in a driverless airplane, right? >> That's right. Or in a driveless light rail. >> Jeff: Right. >> Or, you know, when somebody else is driving they're fine with the driver who's driving. You just sit in the driver's car. >> But there's that one pesky autonomous car problem, when the pedestrian won't go. >> Yeah. >> And the car is stopped it's like a friendly battle-lock. >> That's right, that's right. >> Well good stuff Janet and always great to see you. I'm sure we will see you very shortly 'cause you are at all the great big data conferences. >> Thank you. >> Thanks for taking a few minutes out of your day. >> Thank you. >> Alright she is Janet George, she is the smartest lady at Western Digital, perhaps in Silicon Valley. We're not sure but we feel pretty confident. I am Jeff Frick and you're watching theCUBE from When IoT meets AI: The Intelligence of Things. We will be right back after this short break. Thanks for watching. (upbeat electronic music)