>> Announcer: Live from Las Vegas, it's theCUBE, covering InterConnect 2017. Brought to you by IBM. >> Hey, welcome back, everyone. Here live at the Mandalay Bay in Las Vegas for theCUBE's three-day exclusive coverage of IBM InterConnect 2017. I'm John Furrier. My co-host, Dave Vellante. Our next guest here is Greg Pepper, head of cloud security architects at Check Point Software Technologies. >> You got it. Good afternoon, gentlemen. >> Welcome, welcome to theCUBE. So, security obviously is big. You're seeing compel all the networks, every company out there is buying security, so there's been a security sprawl. But now you guys have a stock that's trading at a very high, 52-week high. Congratulations. >> Yeah, thank you. You know, some people forget about us. We've been doing this for 24 years, we've been the leaders in this industry for over two decades, but sometimes, we're the best kept secret in the industry. >> Unleash some of those secrets here. I know you guys probably can't go into too much secret sauce as a public company, but what's the software secret? Obviously, relationship with IBM is part of why you're here, but what's the Check Point secret sauce right now? >> I think first and foremost, we've built upon a legacy for the last 20 years. We didn't just acquire technology through acquisition, duct tape and paper clips and call it an architecture for our customers. We've built upon a consistent common platform building on our core strengths. I think the second thing that really differentiates us from some of the other guys you mentioned is our commitment and focus to security first. We are a security company end to end, and everything we do is built off of those tenets. And especially with the growth in security in the data center, its migration to cloud, the industry has kind of come back around to software, and though for a while we delivered hardware appliance to customers, 'cause it was the preferred consumption model, when customers go to the cloud, whether it's SoftLayer, Azure, Amazon, Google, and others, we don't have hardware to bring with you, so you need a software defined security strategy to play in the cloud today. >> What is that software defined security strategy? What's the hottest product that you guys have that's working best? >> Everything we have built on our core competencies of management and the gateways themselves. But these days, it's not enough to just be a firewall vendor, so advanced threat prevention, the ability to both prevent and detect malware from getting on the network, rather than just alerting you that something bad happened. We're providing additional access controls with data awareness. I don't need to plug into the network to tell you people are going to YouTube, Netflix, but what's the information about your organization that's being posted out there? Those are the interesting things that we can help differentiate and alert customers to what's going on. >> So, the perimeter's, with the cloud, all these APIs, microservices coming down the pike with cloud, that's the challenge. I mean, this whole idea of being data and software focused. How do you guys play in that world, and what's this focus there? >> The biggest change is moving away from the traditional management architecture to one that's driven by code. These days especially in the cloud to be agile with dev-ops, you have to have security be able to be deployed, programmed, managed, and monitored all through an API, and this is something over the last few years we've enhanced our products to enable automatic deployment in the cloud providers, automatic management, and also integration with people like IBM QRadar in a highly automated way. >> The big discussion in the last couple years in security has been, hey, it's not enough just to dig a moat around the castle. The queen wants to leave her castle, so we've got to, security's got to be everywhere, it's got to follow the data, and also response is another major focus of discussion, we've got to shift spending there. How has that impacted, first of all, you buy that, second of all, how has that impacted your business and your strategy? >> We definitely do agree, which is why as part of our end to end security strategy, the laptops, the desktops, the mobile devices is an area of increased focus for us. Where really just having the traditional perimeter alone is not adequate. The second thing we started to talk about is the ability to move into the cloud. A lot of the competitive solutions out there don't play as well in the cloud because they're dependent on proprietary hardware. If you're a vendor that has custom ASICs, well, you don't have those ASICs when you go to the cloud. Whereas for us, our software defined security strategy, when we go to Amazon, Azure, SoftLayer, and other cloud providers, 100% of our core capabilities moves along with us. >> Talk that through the value proposition and the customer impact. So, it's more flexibility. Is it lower cost, is it speed, is it better response? >> I believe the primary driver for cloud adoption is agility, not always cost savings, although in some cases that is the case. However, the ability to grow and shrink on demand. In the past, our traditional enterprise customers would consume technology for their max resources. If I'm a large department store, I need to be able to handle Black Friday. Well, that's one week a year that you need that peak utilization. That ability to scale up and scale down is one of the major things driving people to the cloud. Well, security has to have the same model. We have to be able to automatically deploy, scale up for those large-scale events, but then also come back down to an average run-time use to help customers save money. >> How about analytics? How does that play into the security business? >> Yeah, I mean look, the whole reason we exist is to give interesting information for technology to be able to chew on, and the ability to provide the forensic auditing accounting for access controls and for our threat prevention, whether it's on the perimeter, in the cloud, in the core, on mobile and end-point devices, there's a reason after 20 years we've been the lead in the industry is 'cause we provide the best forensics data and integration with all the major leading SIM vendors out there. >> Yeah, the 20-year stair with Check Point. Obviously, the company's evolved a lot since then. Talk about the relationship with IBM, obviously we're here at IBM InterConnect, what are you guys doing with IBM? >> IBM's one of our best partners for over the last two decades. For over 18 years now, they've been a customer, a reseller, and a managed services security partner, so there's multiple organization within IBM that have relationship with Check Point to help secure the corporate assets, customer projects in our managed data centers, or even just purely security managed services. One of the exciting projects that we've been working on that was demonstrated at the security booth was an automated security deployment for the hybrid cloud, where the IBM team worked with us to help take security, automatically roll it out into Amazon and Azure, but also bring it into their MSS environment, their managed security services with zero touch, and they're able to provision, have it managed, monitored, and ready to rock and roll in less than 30 seconds. >> And they were doing that all in software? >> Greg: 100% in software, 100% in code with no human intervention. >> So take us through some of those use cases going forward. As you go talk to customers with IBM or on your own, you write on a lot of white board, I can imagine, so what are some of the white board conversations you're having, 'cause security architecture's one of these, kind of a moving train right now. What are some of the patterns you're seeing right now? >> First and foremost, there's a lot of cloud novice, this is new for all of us. So in the walk-jog-run mentality, we all need to come up with the basic terminology and fundamentals so we can have a more advanced conversation. Once we provide the basic knowledge transfer, the second step is how can you help me lift this legacy application and move it to a cloud-centric application, yet still give me the same levels of security and visibility, 'cause I can't go to the board and tell 'em, "Oh, we screwed up. "We moved to the cloud, and now our apps are not secure." As a matter of fact, for our largest customers, the most critical applications will not move to the cloud unless they have a clearly defined security strategy in place. >> So you lay out those parameters up front, then you kind of walk through it, I'd say crawl, walk, run, then jog. >> Greg: Absolutely. >> However you had it, but I mean, lot of people are kind of crawling, but now also, multi-cloud's a big theme here. So now, you're looking at multiple clouds, and some workloads might make sense for cloud one, two, or three depending on the workloads, but some stay on prem. >> 100%. >> And now you got the true private cloud trend where I'm going to have a cloud-like environment on prem. That's cool, development environment looks the same as the cloud, but I got multiple clouds. How do you guys deal with the multi-cloud and this idea of being consistent on prem and on cloud? >> First and foremost, being a software defined gateway, we have this unique capabilities that's the same on premise, Amazon, Azure, Google, SoftLayer, and others as well. Since we're not dependent upon hardware, we have consistent capabilities across all the clouds. The second thing I want to add is from a management perspective, we've built, excuse me, tight integrations with all the data center and cloud providers, so we're able to trust Amazon, VMware, Cisco, OpenStack, Google, and others and real-time integrate their applications and objects and metadata into our security policies, further tightening the integration and automation capabilities between those cloud providers. >> So, you're actively working with all the clouds to integrate in tightly to manage the security. You become the Switzerland for-- >> Look, we were the first of the major security vendors to both be in Amazon and Azure. We were the first achieve Amazon security competency. We were the first to support basic things like clustering and scale set support, which has been a very common deployment in the cloud as well. We've been in this cloud game for the last seven or eight years now, or as I like to joke, we've cloud up-times longer than some of my competitors have been in business. >> Microsoft was actually down on the cloud. We published a report today on siliconangle.com. Three cloud vendors down in a week. I'll give Amazon a little week there, but it's still, you're still going to see some these bumps in the road, but security, you can't have bumps, you got to be rock solid. >> The thing with today in cloud, whether it's the application, the servers, the storage and securities, you have to anticipate for that total failure situation. Heaven forbid, what happens if an east region went down? Case in point, when Amazon had their storage outage, Netflix was not interrupted at all. Now, other organizations that were only deployed in a single region, we were impacted. This is where, I think from an application architecture, one, we have to think beyond single region, single cloud provider. We have to anticipate the total catastrophic failure and how does our business continuity and disaster recovery work. And then, security has to be an integral portion of that. We can't bolt it on after the fact, it's got to be part of the foundation. >> Greg, great point. And by having software, gives you so much flexibility, I love that hybrid cloud example, but I want to get your thoughts on what you said earlier about lift and shift. That seems to be the parlance of the generation. It used to be rip and replace on the enterprise side, but that's not as easy as it is. To your point, you can't just throw it to the cloud, you might have some gaps. As people look to lift and shift, which I always say is be careful, you got to have some concerns. How do you advise your customers when you say, "Hey, we're lifting and shifting to the cloud." >> For those people, I say don't bother. Right, if I'm going to move the same applications and same products and processes from my private data center to the cloud, why bother? If we're not taking advantage of the agility, elasticity, automation, and all the benefits that clouds has to offer, companies should be building new cloud-ready applications for the cloud. We should not just be lifting our legacy applications and like for like moving them to the cloud, 'cause we're not going to get the benefit in return on investment. >> And it's risky, too, by the way. I would agree with you. So, net new applications, no brainer. If the cloud's available, why not? >> Absolutely. >> Let's go back to the workload. Some clouds have better, like analytics use case is a great cloud, just throw IOT data into Amazon or Azure or Office 365 is Azure, and Amazon gets Kinesis, good stuff, and you've got Bluemix over here. You're starting to see that swim lanes of the different vendors. How do you view the differentiation between the vendors, and how do you advise customers? "Hey Greg, I don't know which cloud to go to. "What's your advice?" >> First and foremost, there's pros and cons to everyone's offering. >> It's kind of like Red Sox, Yankees, you know. It's like trying to-- >> Well, let's stop right there, Yankees for sure. >> Dave: You think? >> Absolutely. >> Dave: You really think? >> Well, maybe not in 2017, but-- >> Who's the Yankees, Microsoft or AWS? >> Microsoft probably the Yankees right now. Then again, from my perspective as a Red Sox fan, I'd say it's a tough call. >> (muttering) is the Yankee-killer. Anywhere, let's... >> Alright, go back. >> We digress. >> What I was I going to make a comment of is look for the adjunct services behind the basics, beyond the basic storage, compute and networking services that everybody has as kind of table stakes. For example, if you're someone who's a very heavy Microsoft Office 365 SharePoint user, you're using their business application suite, well, probably migration to Azure is a more natural transition, right. People who are similarly in the Google environment and using the Google suite of applications, it's a benefit to moving the applications there. And to be honest, people who are purely just into the raw compute horsepower and probably the most mature and largest cloud platform, well, Amazon has probably got a five-year head start on the rest of the guys. So, we try not to sit here and determine which of the three clouds is better, 'cause for us, we play in all of them, and our security footprint has to be consistent across all of them. I'll share with you an anecdotal use case from one of my retail customers is building a commerce platform in AWS. But all the corporate applications are moving to Azure, and separately now, they're looking at Google for other global applications as well. So for them, they're going to be in all three cloud providers, just with different applications finding more natural homes. >> Justin Youngblood was just on. He said, the IBM data said 70% of all organizations, or 70% of the organizations have three or more clouds, infrastructure clouds, right. >> I would believe that. >> Back to the security, I mean, the market's booming. In a way, it's unfortunate that the market's booming is 'cause it's such a huge problem that doesn't end. It's great for you. Each year, we look back at last year and say, okay, we feel more secure, and we don't. So, what's happening in the market? Are we finally going to get a handle on sort of how to deal with this, or is it just always going to be this good guy, bad guy, leap-frogging sort of endless loop? >> The big change these days are the bad guys are pros. This is their full-time job, they're very well funded, trained, and able. >> Dave: And they only have to succeed once. >> And remember, the cost of defense is exponentially higher than the cost of offense. So what it costs my banks and hospitals to secure their environment is 10 to 100-fold over what it costs the bad guys, either in the U.S. or some other nation-state, to attack those environments. I think the biggest challenge that most of our customers face, to be honest, is technology saturation. They've bought every product known to mankind. As I like to joke, for every threat, there's an app for that, and most of our customers have bought all three of them. But then they struggle operationally with the technology, and this is more of a people and a process issue than it is a product issue. There's a lot of great technology out there, ours and other vendors as well, but if it's not implemented and maintained properly, those potentially represent the weakest links. >> And there's new threats emerging, ransomware, for instance, is to your point they're overmanned, and the cost to even compare, or defend against that, but they're already hacked. They'll pay the ransom in bit coin to get their stuff back. >> And look, it's cheaper, quicker, and faster to maybe just whack the system and try and do some forensics clean-up than deploy a next generation end-point to try and detect and mitigate against ransomware, disk encryption, or other bots that may get on the end-points themselves. >> But I almost feel like the mitigation, I mean, you've got to have perimeter security, obviously, and continue to invest in that, but I feel like you're never going to stop somebody from penetrating your organization. What's the status on average, the company's penetrated for 200 and whatever end days before they know? 220, 250, whatever number you want. There's got to be more investment in remediating, responding, managing that complexity. And so, I guess the answer to my earlier question was, well, not any time soon. We're going to have to continue to invest in new approaches, new methodologies to deal with this inundation of data, which isn't going to subside. >> Well, but part of it too is in the past, most of the security controls that companies invested in, they put at the perimeter. So, they're overprotecting on the perimeter, but now, the attacks are coming in through the side door. Spearfishing attempts >> Dave: Or internally. >> They're coming in from laptops or mobile devices that leave the organization and come back in, and since most customers lack internal segmentation, a very small infection becomes a very big problem very quickly. So, a lot of customers now are trying to figure out how do I take what I've done in the perimeter and treat my data center, my campus as untrusted, segment and silo and create smaller fault-isolation domains so that heaven forbid there is a breach or an outbreak, it's contained to a smaller subzone, rather than, look at the Target situation, which came in from an HVAC vendor, moved into a payment system, and then exfiltrated millions of credit card records. >> And, or, and not or, but, and techniques to allow the response to focus on the things that matter, and like you said, organizations, CCOS, are inundated with technology, and they don't know necessarily which threats to go deal with. They've got so much data, and to the extent that they can narrow down those high value threats, that's going to help solve the problem. That's why I was asking the question about analytics before. >> That's where I think the partnership with IBM is so important for us, right, 'cause both what they do with Watson and big data analytics and QRadar as well, it's one thing to just create a bunch of alerts, but for most customers, that's a lot of noise. Give me the interesting bits of information. I don't care about these 10 million alerts over the last week. What are the most critical things that my team needs to address right now? And those are the things that collectively IBM and Check Point help. >> How about the competitive landscape? And you guys are kickin' butt, you're well over a billion, what, $1.7 billion company, roughly? >> A little more, but yeah. >> A little more than that, almost a $20 billion market cap, which you said earlier, John, stocks almost at an all-time high, so obviously compete with Palo Alto. Do you compete with HPE, with ArcSight a little bit? I mean, that acquistion, they sort of, that's-- >> They jettisoned some of their core products that were competitive, like TippingPoint. They've kept some of their ArcSight and other big data analytics, the drive service and storage and services out there. But they're as much a partner as they are a competitor. >> Dave: They are? Okay. >> I mean, I would say the usual competitive suspects, some of the guys you mentioned, some of the big route switch vendors like a Cisco or a Juniper out there. Actually, we're in the end-point mobile space as well, which brings in the Symantec and McAfee and Kaspersky. >> And so, right, okay, so what's your big differentiation? >> I think first and foremost is that we have an enterprise management solution that goes from the mobile to the end-point to the cloud to the network. We do it all through a singular console. We have the most scalable security platform in the marketplace today, and to be honest, we have the best security solution out there, both in terms of the effectiveness as well as the manageability. >> Dave: And you're profitable and you're growing. I'm going to throw that in. >> Greg: We've been profitable since day one. >> Greg, thanks for coming onto theCUBE. We really appreciate, give you the final word on the segment as the outlook going forward. Obviously, all the cloud vendors, you work with them all, all trying to be enterprise-ready. >> Yes. >> And they're all, we're the enterprise cloud. Amazon's now the enterprise cloud, Google was flaunting it at Google Next, they got some work to do. IBM certainly is in the enterprise, Oracle's in the enterprise, Microsoft's in the enterprise. Enterprise readiness and the next few years as security evolves, what are the key table stakes that the cloud guys need to continue to work on, continue to invest in, continue to innovate? >> I think the first thing, and this is across all technology, not just cloud, is that interoperability is the new best of breed. All of our customers are going to have a couple of trusted partners. No one enterprise is single-vendor end to end. But we have to be able to play nicely in the sandox. So, whether it's working with Cisco or McAfee or Microsoft or Symantec, if I don't work well with the other investments my companies and customers have invested in, they're not going to have me around for very long. >> And that's the truth. And multi-cloud, and workloads will fit best, 'cause the SaaS also defines some of these big cloud vendors as well. Microsoft SaaS is Office 365, if you have Microsoft, that's going to be some things for ya. Greg, thanks so much, appreciate it. Great commentary with Check Point Software Technologies, talking security, head of architecture here. Greg Pepper, thanks for joining us. This is theCUBE, more live coverage here, day three coverage from theCUBE after this short break. (electronic keyboard music)

>>Okay, welcome back everyone. To the cubes coverage here, coop con cloud native con 2021 in person. The Cuba's here. I'm John farrier hosted the queue with Dave Nicholson, my cohost and cloud analyst, man. It's great to be back, uh, in person. We also have a hybrid event. We've got two great guests here, the founders of deep fence, sham, Krista Swami, C co-founder and CTO, and said deep line founder. It's great to have you on. This is a super important topic. As cloud native is crossed over. Everyone's talking about it mainstream, blah, blah, blah. But security is driving the agenda. You guys are in the middle of it. Cutting edge approach and news >>Like, like we were talking about John, we had operating at the intersection of the awesome desk, right? Open source security and cloud cloud native, essentially. Absolutely. And today's a super exciting day for us. We're launching something called track pepper, Apache V2, completely open source. Think of it as an x-ray or MRI scan for your cloud scan, you know, visualize this cloud at scale, all of the modalities, essentially, we look at cloud as a continuum. It's not a single modality it's containers. It's communities, it's William to settle we'll list all of them. Co-exist side by side. That's how we look at it and threat map. It essentially allows you to visualize all of this in real time, think of fed map, but as something that you, that, that takes over the Baton from the CIS unit, when the lift shift left gets over, that's when the threat pepper comes into picture. So yeah, super excited. >>It's like really gives that developer and the teams ops teams visibility into kind of health statistics of the cloud. But also, as you said, it's not just software mechanisms. The cloud is evolving, new sources being turned on and off. No one even knows what's going on. Sometimes this is a really hidden problem, right? Yeah, >>Absolutely. The basic problem is, I mean, I would just talk to, you know, a gentleman 70 of this morning is two 70 billion. Plus public cloud spent John two 70 billion plus even 3 billion, 30 billion they're saying right. Uh, projected revenue. And there is not even a single community tool to visualize all the clouds and all the cloud modalities at scale, let's start there. That's what we sort of decided, you know what, let's start with utilizing everything else there. And then look for known badness, which is the vulnerabilities, which still remains the biggest attack vector. >>Sure. Tell us about some of the hood. How does this all work cloud scale? Is it a cloud service managed service it's code? Take us out, take us through product. Absolutely. >>So, so, but before that, right, there's one small point that Sandeep mentioned. And Richard, I'd like to elaborate here, right? He spoke about the whole cloud spending such a large volume, right? If you look at the way people look at applications today, it's not just single clone anymore. It's multicloud multi regions across diverse plants, right? What does the solution to look at what my interests are to this point? That is a missing piece here. And that is what we're trying to tackle. And that is where we are going as open source. Coming back to your question, right? How does this whole thing work? So we have a completely on-prem model, right? Where customers can download the code today, install it. It can bill, we give binary stool and Shockley just as the exciting announcement that came out today, you're going to see somewhat exciting entrepreneurs. That's going to make a lot more easy for folks out there all day. Yeah, that's fine. >>So how does this, how does this all fit into security as a micro service and your, your vision of that? >>Absolutely. Absolutely. You know, I'll tell you, this has to do with the one of the continual conferences I would sort of when I was trying to get an idea, trying to shape the whole vision really? Right. Hey, what about syncretism? Microservice? I would go and ask people. They mentioned that sounds, that makes sense. Everything is becoming a microservice. Really. So what you're saying is you're going to deploy one more microservice, just like I deploy all of my other microservices. And that's going to look after my microservices. That compute back makes logical sense, essentially. That was the Genesis of that terminology. So defense essentially is deployed as a microservice. You go to scale, it's deployed, operated just like you to your microservices. So no code changes, no other tool chain changes. It just is yet another microservice. That's going to look after you talk about >>The, >>So there's one point I would like to add here, which is something very interesting, right? The whole concept of microservice came from, if you remember the memo from Jeff Bezos, that everybody's going to go, Microsoft would be fired. That gave rise to a very conventional unconditionally of thinking about their applications. Our deep friends, we believe that security should be. Now. You should bring the same unconventional way of thinking to security. Your security is all bottom up. No, it has to start popping up. So your applications on microservice, your security should also be a micro. >>So you need a microservice for a microservice security for the security. You're starting to get into a paradigm shift where you starting to see the API economy that bayzos and Amazon philosophy and their approach go Beanstream. So when I got to ask you, because this is a trend we've been watching and reporting on the actual application development processes, changing from the old school, you know, life cycle, software defined life cycle to now you've got machine learning and bots. You have AI. Now you have people are building apps differently. And the speed of which they want to code is high. And then other teams are slowing them down. So I've heard security teams just screw people over a couple of days. Oh my God, I can wait five days. No, it used to be five weeks. Now it's five days. They think that's progress. They want five minutes, the developers in real time. So this is a real deal optimum. >>Well, you know what? Shift left was a good thing. Instill a good thing. It helps you sort of figure out the issues early on in the development life cycle, essentially. Right? And so you started weaving in security early on and it stays with you. The problem is we are hydrating. So frequently you end up with a few hundred vulnerabilities every time you scan oftentimes few thousand and then you go to runtime and you can't really fix all these thousand one. You know? So this is where, so there is a little bit of a gap there. If you're saying, if look at the CIC cycle, the in financial cycle that they show you, right. You've got the far left, which is where you have the SAS tools, snake and all of that. And then you've got the center where, which is where you hand off this to ops. >>And then on the right side, you've got tech ops defense essentially starts in the middle and says, look, I know you've had thousand one abilities. Okay. But at run time, I see only one of those packages is loaded in memory. And only that is getting traffic. You go and fix that one because that's going to heart. You see what I'm saying? So that gap is what we're doing. So you start with the left, we come in in the middle and stay with you throughout, you know, till the whole, uh, she asks me. Yeah, well that >>Th that, that touches on a subject. What are the, what are the changes that we're seeing? What are the new threats that are associated with containerization and kind of coupled with that, look back on traditional security methods and how are our traditional security methods failing us with those new requirements that come out of the microservices and containerized world. And so, >>So having, having been at FireEye, I'll tell you I've worked on their windows products and Juniper, >>And very, very deeply involved in. >>And in fact, you know what I mean, at the company, we even sold a product to Palo Alto. So having been around the space, really, I think it's, it's, it's a, it's a foregone conclusion to say that attackers have become more sophisticated. Of course they have. Yeah. It's not a single attack vector, which gets you down anymore. It's not a script getting somewhere shooting who just sending one malicious HTP request exploiting, no, these are multi-vector multi-stage attacks. They, they evolve over time in space, you know? And then what happens is I could have shot a revolving with time and space, one notable cause of piling up. Right? And on the other side, you've got the infrastructure, which is getting fragmented. What I mean by fragmented is it's not one data center where everything would look and feel and smell similar it's containers and tuberosities and several lessons. All of that stuff is hackable, right? So you've got that big shift happening there. You've got attackers, how do you build visibility? So, in fact, initially we used to, we would go and speak with, uh, DevSecOps practitioner say, Hey, what is the coalition? Is it that you don't have enough scanners to scan? Is it that at runtime? What is the main problem? It's the lack of visibility, lack of observability throughout the life cycle, as well as through outage, it was an issue with allegation. >>And the fact that the attackers know that too, they're exploiting the fact that they can't see they're blind. And it's like, you know what? Trying to land a plane that flew yesterday and you think it's landing tomorrow. It's all like lagging. Right? Exactly. So I got to ask you, because this has comes up a lot, because remember when we're in our 11th season with the cube, and I remember conversations going back to 2010, a cloud's not secure. You know, this is before everyone realized shit, the club's better than on premises if you have it. Right. So a trend is emerged. I want to get your thoughts on this. What percentage of the hacks are because the attackers are lazier than the more sophisticated ones, because you see two buckets I'm going to get, I'm going to work hard to get this, or I'm going to go for the easy low-hanging fruit. Most people have just a setup that's just low hanging fruit for the hackers versus some sort of complex or thought through programmatic cloud system, because now is actually better if you do it. Right. So the more sophisticated the environment, the harder it is for the hackers, AK Bob wire, whatever you wanna call it, what level do we cross over? >>When does it go from the script periods to the, the, >>Katie's kind of like, okay, I want to go get the S3 bucket or whatever. There's like levels of like laziness. Yeah. Okay. I, yeah. Versus I'm really going to orchestrate Spearfish social engineer, the more sophisticated economy driven ones. Yeah. >>I think, you know what, this attackers, the hacks aren't being conducted the way they worked in the 10, five years ago, isn't saying that they been outsourced, there are sophisticated teams for building exploiters. This is the whole industry up there. Even the nation, it's an economy really. Right. So, um, the known badness or the known attacks, I think we have had tools. We have had their own tools, signature based tools, which would know, look for certain payloads and say, this is that I know it. Right. You get the stuff really starts sort of, uh, getting out of control when you have so many sort of different modalities running side by side. So much, so much moving attack surfaces, they will evolve. And you never know that you've scanned enough because you never happened because we just pushed the code. >>Yeah. So we've been covering the iron debt. Kim retired general, Keith Alexander, his company. They have this iron dome concept where there's more collective sharing. Um, how do you see that trend? Because I can almost imagine that the open-source man is going to love what you guys got. You're going to probably feed on it, like it's nobody's business, but then you start thinking, okay, we're going to be open. And you have a platform approach, not so much a tool based approach. So just give me tools. We all know that when does it, we cross over to the Nirvana of like real security sharing. Real-time telemetry data. >>And I want to answer this in two parts. The first part is really a lot of this wisdom is only in the community. It's a tribal knowledge. It's their informal feeds in from get up tickets. And you know, a lot of these things, what we're really doing with threat map, but as we are consolidating that and giving it out as a sort of platform that you can use, I like to go for free. This is the part you will never go to monetize this. And we are certain about disaster. What we are monetizing instead is you have, like I said, the x-ray or MRI scan of the cloud, which tells you what the pain points are. This is feel free. This is public collective good. This is a Patrick reader. This is for free. It's shocking. >>I took this long to get to that point, by the way, in this discussion. >>Yeah, >>This is this timing's perfect. >>Security is collective good. Right? And if you're doing open source, community-based, you know, programs like this is for the collector group. What we do look, this whole other set map is going to be open source. We going to make it a platform and our commercial version, which is called fetch Stryker, which is where we have our core IP, which is basically think about this way, right? If you figured out all the pain points and using tech map, or this was a free, and now you wanted the remedy for that pain feed to target a defense, we targeted quarantining of those statin workloads and all that stuff. And that's what our IP is. What we really do there is we said, look, you figured out the attack surface using tech fabric. Now I'm going to use threat Stryker to protect their attacks and stress >>Free. Not free to, or is that going to be Fort bang? >>Oh, that's for, okay. >>That's awesome. So you bring the goodness to the party, the goods to the party, again, share that collective, see where that goes. And the Stryker on top is how you guys monetize. >>And that's where we do some uniquely normal things. I would want to talk about that. If, if, if, if you know public probably for 30 seconds or so unique things we do in industry, which is basically being able to monitor what comes in, what goes out and what changes across time and space, because look, most of the modern attacks evolve over time and space, right? So you go to be able to see things like this. Here's a party structure, which has a vulnerability threats. Mapper told you that to strike. And what it does is it tells you a bunch of stress has a vulnerable again, know that somebody is sending a Melissa's HTP request, which has a malicious payload. And you know what, tomorrow there's a file system change. And there is outbound connection going to some funny place. That is the part that we're wanting this. >>Yeah. And you give away the tool to identify the threats and sell the hammer. >>That's giving you protection. >>Yeah. Yeah. Awesome. I love you guys love this product. I love how you're doing it. I got to ask you to define what is security as a microservice. >>So security is a microservice is a deployment modality for us. So defense, what defense has is one console. So defense is currently self posted by the customers within the infrastructure going forward. We'll also be launching a SAS version, the cloud version of it. But what happens as part of this deployment is they're running the management console, which is the gooey, and then a tiny sensor, which is collecting telemetric that is deployed as a microservice is what I'm saying. So you've got 10 containers running defenses level of container. That's, that's an eight or the Microsoft risk. And it utilizes, uh, EDP F you know, for tracing and all that stuff. Yeah. >>Awesome. Well, I think this is the beginning of a shift in the industry. You start to see dev ops and cloud native technologies become the operating model, not just dev dev ops are now in play and infrastructure as code, which is the ethos of a cloud generation is security is code. That's true. That's what you guys are doing. Thanks for coming on. Really appreciate it. Absolutely breaking news here in the queue, obviously great stuff. Open source continues to grow and win in the new model. Collaboration is the cube bringing you all the cover day one, the three days. I'm Jennifer, your host with Dave Nicholson. Thanks for watching.

>> Narrator: Live from Las Vegas, it's theCUBE. Covering Oracle Modern Customer Experience, 2017. Brought to you by, Oracle. (upbeat music) >> Welcome back to theCUBE. I'm Peter Burris, and once again theCUBE is here at Oracle Modern Marketing... Modern Customer Experience, having a great series of conversations about the evolution of marketing, the role technology is playing, and especially important, the centerpiece that data now has within a overall orientation towards customer experience. Now one of the key features of that notion of customer experience is what's going on with service. And this is a great session, because we've got a representative from Oracle, but also a customer, as well. Welcome to Steve Fioretti, who's the VP of Product Management, Oracle Service Cloud and Heather Miksch, who's the Vice President of Field and Product Operations at Carbon. >> Thank you. >> Peter: Welcome to the (mumbles) >> Thanks. >> Glad to be here. >> So, Steve why don't we start with you. >> Steve: Sure. >> Oracle is here talking about how the cloud can help transform field and service operations. >> Steve: Right. >> How is it transforming it, what're the trends? >> Well, there's a lot of interesting trends that are affecting customer service, and I would, you talked about marketing and a lot of people say customer service is the new marketing. A lot of, a lot of interactions that people have with a company is in the customer service group and that really affects their impact on the brand. And there's a lot of things going on in the industry that are affecting customer service. There's new dynamic channels emerging, for example, people want to use Facebook Messenger, or WeChat, or WhatsApp as customer service channels to interact with their brand. It's much beyond just email, phone, chat, things like that. So, new channels are emerging and companies have to think about how do I integrate that into my customer service organization. Automation has really come into the fore. So, you know, in our personal lives we use Siri, and other V, you know, interactions we have with Alexa. So, those are coming into businesses to automate those, perhaps more simple, customer service processes. The internet of things is really taking off, where connected devices are allowing organizations to deliver predictive and proactive service. And on the automation front, they're even extending to where organizations are taking robotics and making robots agents in a retail store, for example. >> Are you talking about me? >> Wow it's Pepper. Hi, Pepper, what are...(Peter laughs) I didn't know you were here, that's awesome. So, Pepper, I'll ask you a question. What makes you a great Customer Service Agent? >> I'm smart, I'm connected, and I'm cool and, most importantly, I'm effective. (Steve laughs) >> And we replaced John Furrier with Pepper. >> Steve: Excellent.(Heather laughs) >> So, going to the next question about the, as we use robotics, as we use many of these things: we have to remember that these are not magic, they're really is no intelligence, in the classical sense, in them, they are still being driven to perform functions, take action, based on the availability of data that is coming off of customers. So talk a bit about the role the data, data integration, and some of these new tools: AI, or Adaptive Intelligence as you're calling it, are playing in ensuring that we can, enhance Customer Experience with new devices, and these new channels. >> You're absolutely right. I mean, if, you know, it's all about making the experience with a device like, like Pepper personalized and effective, and data, knowing what a consumer wants, what their preferences, and perhaps anticipating their preferences before, you know, they even know that; their past buying history, and taking all that, first-party data and third-party data, combining that with artificial intelligence, to deliver those personalized smart experiences is what's really happening. You heard a lot at this conference about Oracle's Adaptive Intelligence Initiative, and in the context of service, we're going to be building applications for things like account health, predictive field service, so, you know, you can predict ahead of time that a machine may, you know, may need service or break. And, you know, our customer here, Heather from Carbon is going to talk a lot about what they're doing with-- >> Well, so-- >> You know, smarts and the experience-- >> Got it, so how does this resonate with Carbon? >> Well, so, Carbon, is a, we manufacture an industrial 3D printer, and we have a process we call Digital Light Synthesis, which allows us to make photo-polymer materials that are robust enough to use in final production. So, our goal is to take customers from their design, of their part, straight into production, using the 3D printer as a means of production. And the reason why this is so exciting to Carbon, is our printer is actually an IOT device. It operates over the internet, and it operates through a browser. As a result, all types of data, from machine data from the printer, are flowing into our databases; as well as operational data, how long is the print taking, what type of resin is the customer using, how often are they printing, are they running into problems with their print? We've also built in a feedback system for the user, directly in the user interface, that flows directly through our channels into our databases, and it actually opens tickets in our Oracle Service Cloud for agents to contact the customers. The way we use this in a very practical standpoint, to give you one example, is for machine failures. The idea that we can monitor our printers in the field, and we can see if a part is having problems, and might fail, and we can actually proactively reach out to the customer and say, "We'd like to be there "in a couple weeks, change out this part. "It's not affecting your machine yet. "It's not affecting your prints." And, the customer is now able, instead of having unplanned downtime, which can be very difficult for a production environment, they now have planned downtime. This technology is nothing new. The example I like to use is, in the nuclear power industry, you don't wait until you have a core meltdown and then call your service engineer.(Steve laughs) Like, it's been around for for decades. >> Form has been around for a while. >> But what's new, is actually taking this technology and putting it in capital equipment, or putting it in devices like Peppper. I mean, she's also an IOT device; or even putting it into some of our wearables, or just other consumer products as well. And once you actually have this data coming through to the manufacturer of the device, it's really almost limitless what you can do with it. And, just in our short time of Carbon actually working on this problem, we have about 70% of our hardware failures are actually predictive. So that we're able to go out and repair the printer before the customer even realizes they have a problem. And some of the problems, we can actually fix before the customer knows anything, and we can fix them remotely from our offices in Redwood City. >> And it's interesting, theCUBE this week was also at the National Association of Broadcasters, in the NEB show, and we actually had an astronaut present over theCUBE. >> Yes, yes. >> One of the things that's interesting is there are 3D printers now on-- >> There are. >> Up on the Space Station. >> Yes, yes. >> So that you can print things a long ways away. That's one of the advantages, one of the great use cases of 3D printers >> Yes. >> Is that you can actually assemble, or you can create and assemble things, in very very, you know, unfriendly environments. >> Yes, yes. So, being able to schedule, and being able to plan that, is absolutely essential. >> Yes, yes and you can see, so for us, for 3D printers, some of the use cases that our customers are coming to us with, is they are companies, their own capital equipment manufacturers that have hundreds of thousands of spare parts, and they don't want to have to keep these inventories of massive spare parts. They want to have a design sent directly to a printer, maybe it's located in another country, closer to the point of use for that part, print out the part, and get it to the user faster. The idea is to actually move, one of the ideas, is to move manufacturing closer to the point of use. So that we're not spending all this time shipping products, you know, across the entire world, when we can actually be producing them much closer to the user. >> So that suggests, when we think about, again, the role of integration, the role of data, the idea of the Service Cloud; that there will be circumstances in which the part is printed and the capital equipment, Lessor, or the person who sold it, is on site to then put it in place, and assemble it. So now we're talking about multiple people operating very very quickly with a lot of new technology. >> Right. >> And, we now see why these types of devices and the need for that data sharing is so crucial. So, how is Oracle, in Oracle's vision of how service is going to be performed in the future, facilitating these types of interactions. >> So, I mean what we have to do is think about the technologies that are powering devices like robots, that are, providing technologies that are powering virtual assistants to automate customer interactions, to deliver technologies that help customers serve themselves. Another example is, more and more people, particularly younger generation, they don't want to phone. You've got a phone in home, they don't want to call you. They don't want to have anything to do with the phone. So, that's why things like messaging, self-service, going to a website and finding their own answer are critical. So, enabling and anticipating the data, the technologies, the way, the channels that people want to use, are all going to allow brands like Carbon and others to deliver great customer service for-- >> How are you using the Oracle Service Cloud, then, to facilitate many of these changes in your organization. >> So right now, what we have is for... We actually have a database we use for our big machine data. So, all the big machine data comes through, all the data coming off of our printers. And then we've integrated that database into Oracle Service Cloud; so then, instead of a customer having to phone up if they have a problem, we actually have, on our user interface, a little button, it just says "Request Help", that's all they need to do, and it's within the print job that they've been working on. All of that data about their print job: who the user is, what the company is, which printer they were using, how long was the print. Any specific information they want to say about the print, like why they're having trouble with it, it flows through into Oracle Service Cloud, and within the Oracle Service Cloud environment we can open up our big machine database, within that same environment, we can look at the actual print job. And then, we have an escalation tool we use for our engineering team. If we need to escalate, we can do that out of Service Cloud as well. And the idea is that there's very little manual entry of any other information. All of that is just flowing through, and everybody within the organization, whether it's the people that are first in front of the customer, or whether it's our engineers, have access to the exact same data. >> But is the system also then, through the escalation process, saying, well, we really got to get someone at the hardware level, or someone here, or someone at the design level. So you're flowing it to the right person. >> Yes, yes, absolutely. And the other fabulous thing about having these internet connected devices, is even when we do need to send somebody out on site to make a hardware fix, because of the diagnostic data we have from the device, we have, until now, 100% success rate in having the right part on-hand. Which is, if you've ever had much experience with capital equipment repairs, or even a repair of your dishwasher, sometimes the people don't have the right parts. We always have the right parts. >> That's too bad you couldn't >> So far, nothing-- >> print the part with the printer when it's down.(laughs) >> That's an interesting thing. We actually do have some parts within our printer that are printed on our printers, so its (laughs) it's pretty fun >> Can I talk about one other short example-- >> Of course. >> Of another customer that actually Heather's met here at the show, Denon & Marantz, so, they make all sorts of audio equipment, high-end audio equipment, and they've got a new brand of speakers, wireless speakers, called HEOS. And, when they first started, selling those to consumers they noticed, these are connected as well, they noticed that a number of them were having, a chip problem, remotely. People were calling in. So they went out, and they, they pinged, if you will, because they're connected, all of their consumer deployments, and they could tell that, you know, a small percentage of them are going to fail. They actually shipped speakers to those consumers before they even knew they had a problem, and they arranged to pick up the old ones, and you can imagine the value the customer, loyalty, and customer sat that that had. So that proactive predictive customer service example in the consumer world, and in a business world, really makes service that much-- >> Yeah. >> So, customer service, increasingly, is taking some degree of responsibility for ensuring that things operate within the threshold, as opposed to fixing things after they've broken. >> Yes, absolutely. >> Exactly. >> Heather: Yes, yeah. >> So how does that tie back into marketing and sales. So, at Carbon what is the, what is the way these feedback loops are being used to also inform marketing and selling. >> So, the interesting thing is that because we're also gathering operational data, we actually use the data coming off our printers for much more than just a service organization. In fact, our entire company is becoming more and more dependent on this printer data. So, for instance, our product group, when they're looking at bringing out a new feature they're actually looking at the data of the actual prints and the features that the customers are currently using, and deciding, do we need to augment this feature? Do we need to bring out another tool for our customers to use? And then looking at the printer data to make those decisions, and to prioritize what projects to work on because as you can imagine we've just got a ton of projects that we'd like to work on, and we need to make some priorities. The other thing that we're looking at is changing customer dynamics. Like we have, all of our customers are broken down into different industries, and we monitor the different printing behaviors, across industries, and we've been surprised. Like, there's certain industries that have grown faster than we would have expected, and because we've got this data that we look at every single day, we're looking at our customers' print data, we can actually make much faster corrections to either marketing campaigns, or sales strategies, or things like that, rather than waiting for a monthly roll-up or a quarterly roll-up or something like that. >> So who's the steward of data within Carbon? >> Who is the steward of data? We actually have a Director of Business Operations, his name is Chris Hutton. He actually works a lot with Oracle. He recently spoke at the Modern Finance Experience with Safra Catz, and I would say that if anyone's the steward of the data, he's probably the Grand Poobah of this data? But many of us have access to it. I mean, I can go into some of these databases and pull all the data I need. We don't really restrict it. >> But he's making sure that every, he's making sure that the data works for everybody in the organization. >> Yeah. Yeah, I'd say to some degree, yes. We also have our software engineers, making sure the printer data is actually-- >> Well, they're always... >> Heather, I think I would... >> Always behind the scenes. >> I think I would like the title Steward of Data. >> Yeah. (laughs) >> I think that's, I think I just found my new title. >> It's a little geeky.(laughs) >> Well it won't be long. Somebody's going to be called, and-- >> Exactly. One other quick example of how that feedback's happening between a customer service experience and let's say marketing, is, back to my Denon & Marantz example. They had another set of speakers, and they can tell, they often, the consumer will label the speaker, based upon, you know, this is the living room, this is the bedroom... And they had some failures on another brand of speakers, and they noticed a commonality, they were all labeled Bathroom. And, basically, they realized that their speakers... Some of these speakers couldn't handle the humidity that was happening in the bathroom; drove that back into product development, built a new series of speakers quickly for bathroom that were more waterproof, >> Yeah. >> Or, more moisture resistant, and created a new product extension that actually sells quite well. So, there's just a simple example of how that data flowed back into product development and marketing. >> So, Heather, you're not feeling like a fish out water here at a customer experience show with all of the-- >> Oh, no, of course not. No, I love this kind of stuff. >> What's exciting you about listening to, mainly marketers, but a lot of customer experience, too? >> I, you know people-- >> Talk about customer service >> That are in service, they get excited. I mean, fundamentally, there's all kinds of reasons for growing the business, and increasing revenue, and cutting costs, and all those things, but fundamentally, people are in service to help other people. Like, that's what gets us up in the morning. That's what makes us jump out of bed. So, the idea that there's all these companies doing these super-cool things, where you can, really, proactively be helping people instead of waiting till they're already in trouble. That's like, you've just burst through a barrier that's existed for millennia; the fact that we can actually start predicting problems. >> But that's also, we also talked a lot here on theCUBE this week about the role that talent's going to play. And, while I've never been in a hardcore customer service job, I know that people who have gone in, often got demoralized because they were always being yelled at because there was problem. >> Yes, yes, yes. >> And I had to believe it's attracting a new class of person because they can actually be participating, and anticipating, and solving problems >> Yes, yes, yes. Well, and I, it does take a certain type to be a customer, to be in front of customers all the time. We always say that the number one rule is you have to hire happy people to be put in that position, because (laughs) >> Peter: So, how about (Heather laughs) >> Actually, that was a very insightful question, because we were on a panel yesterday with an analyst, Denis Pombriant from the Beagle Research and he talked about, well, a couple of dynamics. One is, agents, the profile of the agents that you hire is changing. Because all the simple things are being solved online through self-service, and now that agent has to be a more gifted, even arguably, he called it a controller, a more aggressive agent that's going to be a problem-solver, able to collaborate with others. So, more empowered, and that's one thing, so I thought your question was really insightful. The nature of that agent is changing. And another thing that smart companies do, is they empower those agents. You know, not just with technology, but they give them the ability to, you know, the a brand of hotels, high-end hotels, I won't use the brand, but their agents are given a couple thousand dollars a day, and are empowered to use that to fix any issues. You know, somebody shows up and the room's booked, they don't drag them out of the hotel. (all laugh) They actually find them... Maybe they upgrade the room or they get them a meal if they have a problem so, empowering them also makes the agent feel much better about delivering customer service-- >> Alright, so Steve Fioretti, VP Product Management Oracle Service Cloud. Heather Miksch the Vice President of Field and Product Operations at Carbon, and Pepper from SoftBank. >> Yay! >> Thank you all for being a part of theCUBE here at the Oracle-- >> Thank you. >> Modern Customer Experience >> Thank you Peter. >> And talking about the role that service is now playing in driving customer experience and the role that the Cloud is playing in improving customer service. >> Steve: Great, awesome. >> We'll be back with a wrap-up in a few minutes, and in fact, John will magically reappear. Give us a few minutes and we'll be back with more from theCUBE. (upbeat music)

>> Welcome everyone. Thank you for coming to the Intel AI lounge and joining us here for this economist world event. My name is Jack. I'm the chief architect of our autonomist driving solutions at Intel and I'm very happy to be here and to be joined by an esteemed panel of colleagues who are joining to, I hope, engage you all in a frayed dialogue and discussion. There will be time for questions as well, so keep your questions in mind. Jot them down so you ask them to us later. So first, let me introduce the panel. Next to me we have Michelle, who's the co-founder and CEO of Fine Mind. She just did an interview here shortly. Fine Mind is a company that provides a technology platform for retailers and brands that uses artificial intelligence as the heart of the experiences that her company's technology provides. Joe from Intel is the head of partnerships and acquisitions for artificial intelligence and software technologies. He participated in the recent acquisition of Movidius, a computer vision company that Intel recently acquired and is involved in a lot of smart city activities as well. And then finally, Sarush, who is data scientist by training, but now has JDA labs, which is researching emerging technologies and their application in the supply chain worldwide. So at the end of the day, the internet things that artificial intelligence really promises to improve our lives in quite incredible ways and change the way that we live and work. Often times the first thing that we think about when we think about AI is Skynet, but we at Intel believe in AI for good and that there's a lot of things that can happen to improve the way people live, work, and enjoy life. So as things in the Internet, as things become connected, smart, and automated, artificial intelligence is really going to be at the heart of those new experiences. So as I said my role is the architect for autonomous driving. It's a common place when people think about artificial intelligence, because what we're trying to do is replace a human brain with a machine brain, which means we need to endow that machine with intelligent thoughts, contexts, experiences. All of these things that sort of make us human. So computer vision is the space, obviously, with cameras in your car that people often think about, but it's actually more complicated than that. How many of us have been in a situation on a two lane road, maybe there's a car coming towards us, there's a road off to the right, and you sort of sense, "You know what? That car might turn in front of me." There's no signal. There's no real physical cue, but just something about what that driver's doing where they're looking tells us. So what do we do? We take our foot off the accelerator. We maybe hover it over the brake, just in case, right? But that's intelligence that we take for granted through years and years and years of driving experience that tells us something interesting is happening there. And so that's the challenge that we face in terms of how to bring that level of human intelligence into machines to make our lives better and richer. So enough about automated vehicles though, let's talk to our panelists about some of the areas in which they have expertise. So first for Michelle, I'll ask... Many of us probably buy stuff online everyday, every week, every hour, hourly delivery now. So a lot has been written about the death of traditional retail experiences. How will artificial intelligence and the technology that your company has rejuvenate that retail experience, whether it be online or in the traditional brick and mortar store? >> Yeah, excuse me. So one of the things that I think is a common misconception. You hear about the death of the brick and mortar store, the growth of e-commerce. It's really that e-commerce is beating brick and mortar in growth only and there's still over 90% of the world's commerce is done in physical brick and mortar store. So e-commerce, while it has the growth, has a really long way to go and I think one of the things that's going to be really hard to replace is the very human element of interaction and connection that you get by going to a store. So just because a robot named Pepper comes up to you and asks you some questions, they might get you the answer you need faster and maybe more efficiently, but I think as humans we crave interaction and shopping for certain products especially, is an experience better enjoyed in person with other people, whether that's an associate in the store or people you come with to the store to enjoy that experience with you. So I think artificial intelligence can help it be a more frictionless experience, whether you're in store or online to get you from point A to buying the thing you need faster, but I don't think that it's going to ever completely replace the joy that we get by physically going out into the world and interacting with other people to buy products. >> You said something really profound. You said that the real revolution for artificial intelligence in retail will be invisible. What did you mean by that? >> Yeah, so right now I think that most of the artificial intelligence that's being applied in the retail space is actually not something that shoppers like you and I see when we're on a website or when we're in the store. It's actually happening behind the scenes. It's happening to dynamically change the webpage to show you different stuff. It's happening further up the supply chain, right? With how the products are getting manufactured, put together, packaged, shipped, delivered to you, and that efficiency is just helping retailers be smarter and more effective with their budgets. And so, as they can save money in the supply chain, as they can sell more product with less work, they can reinvest in experience, they can reinvest in the brand, they can reinvest in the quality of the products, so we might start noticing those things change, but you won't actually know that that has anything to do with artificial intelligence, because not always in a robot that's rolling up to you in an aisle. >> So you mentioned the supply chain. That's something that we hear about a lot, but frankly for most of us, I think it's very hard to understand what exactly that means, so could you educate us a bit on what exactly is the supply chain and how is artificial intelligence being implied to improve it? >> Sure, sure. So for a lot of us, supply chain is the term that we picked up when we went to school or we read about it every so often, but we're not that far away from it. It is in fact a key part of what Michelle calls the invisible part of one's experience. So when you go to a store and you're buying a pair of shoes or you're picking up a box of cereal, how often do we think about, "How did it ever make it's way here?" We're the constituent components. They probably came from multiple countries and so they had to be manufactured. They had to be assembled in these plants. They had to then be moved, either through an ocean vessel or through trucks. They probably have gone through multiple warehouses and distribution centers and then finally into the store. And what do we see? We want to make sure that when I go to pick up my favorite brand of cereal, it better be there. And so, one of the things where AI is going to help and we're doing a lot of active work in this, is in the notion of the self learning supply chain. And what that means is really bringing in these various assets and actors of the supply chain. First of all, through IOT and others, generating the data, obviously connecting them, and through AI driving the intelligence, so that I can dynamically figure out the fact that the ocean vessel that left China on it's way to Long Beach has been delayed by 24 hours. What does that mean when you go to a Foot Locker to buy your new pair of shoes? Can I come up with alternate sourcing decisions, so it's not just predicting. It's prescribing and recommending as well. So behind the scenes, bringing in a lot of the, generating a lot of the data, connecting a lot of these actors and then really deriving the smarts. That's what the self learning supply chain is all about. >> Are supply chains always international or can they be local as well? >> Definitely local as well. I think what we've seen over the last decades, it's kind of gotten more and more global, but a lot of the supply chain can really just be within the store as well. You'd be surprised at how often retailers do not know where their product is. Even is it in the front of the store? Is it in the back of the store? Is it in the fitting room? Even that local information is not really available. So to have sensors to discover where things are and to really provide that efficiency, which right now doesn't exist, is a key part of what we're doing. >> So Joe, as you look at companies out there to partner or potentially acquire, do you tend to see technologies that are very domain specific for retail or supply chain or do you see technologies that could bridge multiple different domains in terms of the experiences we could enjoy? >> Yeah, definitely. So both. A lot of infant technologies start out in very niched use cases, but then there are technologies that are pervasive across multiple geographies and multiple markets. So, smart cities is a good way to look at that. So let's level set really quick on smart cities and how we think about that. I have a little sheet here to help me. Alright, so, if anybody here played Sim City before, you have your little city that's a real world that sits here, okay? So this is reality and you have little buildings and cars and they all travel around and you have people walking around with cell phones. And what's happening is as we develop smart cities, we're putting sensors everywhere. We're putting them around utilities, energies, water. They're in our phones. We have cameras and we have audio sensors in our phones. We're placing these on light poles, which is existing sustaining power points around the city. So we have all these different sensors and they're not just cameras and microphones, but they're particulate sensors. They're able to do environmental monitoring and things like that. And so, what we have is we have this physical world with all these sensors here. And then what we have is we've created basically this virtual world that has a great memory because it has all the data from all the sensors and those sensors really act as ties, if you think of it like a quilt, trying a quilt together. You bring it down together and everywhere you have a stitch, you're stitching that virtual world on top of the physical world and that just enables incredible amounts of innovation and creation for developers, for entrepreneurs, to do whatever they want to do to create and solve specific problems. So what really makes that possible is communications, connectivity. So that's where 5G comes in. So with 5G it's not just a faster form of connectivity. It's new infrastructure. It's new communication. It includes multiple types of communication and connectivity. And what it allows it to do is all those little sensors can talk to each other again. So the camera on the light pole can talk to the vehicle driving by or the sensor on the light pole. And so you start to connect everything and that's really where artificial intelligence can now come in and sense what's going on. It can then reason, which is neat, to have computer or some sort of algorithm that actually reasons based on a situation that's happening real time. And it acts on that, but then you can iterate on that or you can adapt that in the future. So if we think of an actual use case, we'll think of a camera on a light post that observes an accident. Well it's programmed to automatically notify emergency services that there's been an accident. But it knows the difference between a fender bender and an actual major crash where we need to send an ambulance or maybe multiple firetrucks. And then you can create iterations and that learns to become more smart. Let's say there was a vehicle that was in the accident that had a little yellow placard on it that said hazard. You're going to want to send different types of emergency services out there. So you can iterate on what it actually does and that's a fantastic world to be in and that's where I see AI really playing. >> That's a great example of what it's all about in terms of making things smart, connective, and autonomous. So Michelle as somebody who has founded the company and the space with technology that's trying to bring some of these experiences to market, there may be folks in the audience who have aspirations to do the same. So what have you learned over the course of starting your company and developing the technology that you're now deploying to market? >> Yeah, I think because AI is such a buzz word. You can get a dot AI domain now, doesn't mean that you should use it for everything. Maybe 7, 10, 15 years ago... These trends have happened before. In the late 90s, it was technology and there was technology companies and they sat over here and there was everybody else. Well that not true anymore. Every company uses technology. Then fast forward a little bit, there was social media was a thing. Social media was these companies over here and then there was everybody else and now every company needs to use social media or actually maybe not. Maybe it's a really bad idea for you to spend a ton of money on social media and you have to make that choice for yourself. So the same thing is true with artificial intelligence and what I tell... I did a panel on AI for Adventure Capitalists last week, trying to help them figure out when to invest and how to evaluate and all that kind of stuff. And what I would tell other aspiring entrepreneurs is "AI is means to an end. "It's not an end in itself." So unless you're a PH.D in machine learning and you want to start an AI as a service business, you're probably not going to start an AI only company. You're going to start a company for a specific purpose, to solve a problem, and you're going to use AI as a means to an end, maybe, if it makes sense to get there, to make it more efficient and all that stuff. But if you wouldn't get up everyday for ten years to do this business that's going to solve whatever problem you're solving or if you wouldn't invest in it if AI didn't exist, then adding dot AI at the end of a domain is not going to work. So don't think that that will help you make a better business. >> That's great advice. Thank you. Surash, as you talked about the automation then of the supply chain, what about people? What about the workers whose jobs may be lost or displaced because of the introduction of this automation? What's your perspective on that? >> Well, that's a great question. It's one that I'm asked quite a bit. So if you think about the supply chain with a lot of the manufacturing plants, with a lot of the distribution centers, a lot of the transportation, not only are we talking about driverless cars as in cars that you and I own, but we're talking about driverless delivery vehicles. We're talking about drones and all of these on the surface appears like it's going to displace human beings. What humans used to do, now machines will do and potentially do better. So what are the implications around human beings. So I'm asked that question quite a bit, especially from our customers and my general perception on this is that I'm actually cautiously optimistic that human beings will continue to do things that are strategic. Human beings will continue to do things that are creative and human being will probably continue to do things that are truly catastrophic, that machines simply have not been able to learn because it doesn't happen very often. One thing that comes to mind is when ATM machines came about several years ago before my time, that displaced a lot of teller jobs in the banking industry, but the banking industry did not go belly up. They found other things to do. If anything, they offered more services. They were more branches that were closed and if I were to ask any of you now if you would go back and not have 24/7 access to cash, you would probably laugh at me. So the thing is, this is AI for good. I think these things might have temporary impact in terms of what it will do to labor and to human beings but I think we as human beings will find bigger, better, different things to do and that's just in the nature of the human journey. >> Yeah, there's definitely a social acceptance angle to this technology, right? Many of us technologists in the room, it's easier for us to understand what the technology is, how it works, how it was created, but for many of our friends and family, they don't. So there's a social acceptance angle to this. So Michelle as you see this technology deployed in retail environments, which is a space where almost every person in every country goes, how do you think about making it feel comfortable for people to interact with this kind of technology and not be afraid of the robots or the machines behind the curtain. >> Yeah, that's a great question. I think that user experience always has to come first, so if you're using AI for AI's sake or for the cool factor, the wow factor, you're already doing it wrong. Again, it needs to solve a problem and what I tend to tell people who are like, "Oh my God. AI sounds so scary. "We can't let this happen." I'm like, "It's already happening "and you're already liking it. "You just don't know "because it's invisible in a lot of ways." So if you can point of those scenarios where AI has already benefited you and it wasn't scary because it was a friendly kind of interaction, you might not even have realized it was there versus something that looks so different and... Like panic driving. I think that's why the driverless car thing is a big deal because you're so used to seeing, in America at least, someone on the left side of the car in the front seat. And not seeing that is like, woah, crazy. So I think that it starts with the experience and making it an acceptable kind of interface or format that doesn't give you that, "Oh my God. Something is wrong here," kind of feeling. >> Yeah, that's a great answer. In fact, it reminds me there was this really amazing study by a Professor Nicholas Eppily that was published in the journal of social psychology and the name of this study was called A Mind In A Machine. And what he did was he took subjects and had a fully functional automated vehicle and then a second identical fully functional automated vehicle, but this one had a name and it had a voice and it had sort of a personality. So it had human anthropomorphics characteristics. And he took people through these two different scenarios and in both scenarios he's evil and introduced a crash in the scenario where it was unavoidable. There was nothing going to happen. You were going to get into an accident in these cars. And then afterwards, he pulled the subjects and said, "Well, what did you feel about that accident? "First, what did you feel about the car?" They were more comfortable in the one that had anthropomorphic features. They felt it was safer and they'd be more willing to get into it, which is not terribly surprising, but the kicker was the accident. In the vehicle that had a voice and a name, they actually didn't blame the self-driving car they were in. They blamed the other car. But in the car that didn't have anthropomorphic features, they blamed the machine. They said there's something wrong with that car. So it's one of my favorite studies because I think it does illustrate that we have to remember the human element to these experiences and as artificial intelligence begins to replace humans, or some of us even, we need to remember that we are still social beings and how we interact with other things, whether they be human or non-human, is important. So, Joe, you talk about evaluating companies. Michelle started a company. She's gotten funding. As you go out and look at new companies that are starting up, there's just so much activity, companies that just add dot AI to the name as Michelle said, how do you cut through the noise and try to get to the heart of is there any value in a technology that a company's bringing or not? >> Definitely. Well, each company has it's unique, special sauce, right? And so, just to reiterate what Michelle was talking about, we look for companies that are really good at doing what they do best, whatever that may be, whatever that problem that they're solving that a customer's willing to pay for, we want to make sure that that company's doing that. No one wants a company that just has AI in the name. So we look for that number one and the other thing we do is once we establish that we have a need or we're looking at a company based on either talent or intellectual property, we'll go in and we'll have to do a vetting process and it takes a whole. It's a very long process and there's legal involved but at the end of the day, the most important thing for the start up to remember is to continue doing what they do best and continue to build upon their special sauce and make sure that it's very valuable to their customer. And if someone else wants to look at them for acquisition so be it, but you need to be meniacally focused on your own customer. That's my two cents. >> I'm thinking again about this concept of embedding human intelligence, but humans have biases right? And sometimes those biases aren't always good. So how do we as technologists in this industry try to create AI for good and not unintentionally put some of our own human biases into models that we train about what's socially acceptable or not? Anyone have any thoughts on that? >> I actually think that the hype about AI taking over and destroying humanity, it's possible and I don't want to disagree with Steven Hawking as he's way smarter than I am. But he kind of recognizes it could go both ways and so right now, we're in a world where we're still feeding the machine. And so, there's a bunch of different issues that came up with humans feeding the machine with their foibles of racism and hatred and bias and humans experience shame which causes them to lash out and what to put somebody else down. And so we saw that with Tay, the Microsoft chatbot. We saw that with even Google's fake news. They're like picking sources now to answer the question in the top box that might be the wrong source. Ads that Google serves often show men high paying jobs, $200,000 a year jobs, and women don't get those same ones. So if you trace that back, it's always coming back to the inputs and the lens that humans are coming at it from. So I actually think that we could be in a way better place after this singularity happens and the machines are smarter than us and they take over and they become our overlords. Because when we think about the future, it's a very common tendency for humans to fill in the blanks of what you don't know in the future with what's true today. And I was talking to you guys at lunch. We were talking about this harbored psychology professor who wrote a book and in the book he was talking about how 1950s, they were imagining the future and all these scifi stories and they have flying cars and hovercrafts and they're living in space, but the woman still stays at home and everyone's white. So they forgot to extrapolate the social things to paint the picture in, but I think when we're extrapolating into the future where the computers are our overlords, we're painting them with our current reality, which is where humans are kind of terrible (laughs). And maybe computers won't be and they'll actually create this Utopia for us. So it could be positive. >> That's a very positive view. >> Thanks. >> That's great. So do we have this all figured out? Are there any big challenges that remain in our industries? >> I want to add a little bit more to the learning because I'm a data scientist by training and a lot of times, I run into folks who think that everything's been figured out. Everything is done. This is so cool. We're good to go and one of the things that I share with them is something that I'm sure everyone here can relate to. So if a kindergartner goes to school and starts to spell profanity, that's not because the kid knows anything good or bad. That is what the kid has learned at home. Likewise, if we don't train machines well, it's training will in fact be biased to your point. So one of the things that we have to kep in mind when we talk about this is we have to be careful as well because we're the ones doing the training. It doesn't automatically know what is good or bad unless that set of data is also fed to it. So I just wanted to kind of add to your... >> Good. Thank you. So why don't we open it up a little bit for questions. Any questions in the audience for our panelists? There's one there looks like (laughs). Emily, we'll get to you soon. >> I had a question for Sarush based on what you just said about us training or you all training these models and teaching them things. So when you deploy these models to the public with them being machine learning and AI based, is it possible for us to retrain them and how do you build in redundancies for the public like throwing off your model and things like that? What are some of the considerations that go into that? >> Well, one thing for sure is training is continuous. So no system should be trained once, deployed, and then forgotten. So that is something that we as AI professionals need to absolutely, because... Trends change as well. What was optimal two years ago is no longer optimal. So that part needs to continue to happen and we're the where the whole IOT space is so important is it will continue to generate relevant consumable data that these machines can continuously learn. >> So how do you decide what data though, is good or bad, as you retrain and evolve that data over time? As a data scientist, how do you do selection on data? >> So, and I want to piggyback on what Michelle said because she's spot on. What is the problem that you're trying to solve? It always starts from there because we have folks who come in to CIOs, "Oh look. "When big data was hot, we started to collect "a lot of the data, but nothing has happened." But data by itself doesn't automatically do magic for you, so we ask, "What kind of problem are you trying to solve? "Are you trying to figure out "what kinds of products to sell? "Are you trying to figure out "the optimal assortment mix for you? "Are you trying to find the shortest path "in order to get to your stores?" And then the question is, "Do you now have the right data "to solve that problem?" A lot of times we put the science and I'm a data scientist by training. I would love to talk about the science, but really, it's the problem first. The data and the science, they come after. >> Thanks, good advice. Any other questions in the audience? Yes, one right up here. (laughing) >> Test, test. Can you hear me? >> Yep. >> So with AI machinery becoming more commonplace and becoming more accessible to developers and visionaries and thinkers alike rather than being just a giant warehouse of a ton of machines and you get one tiny machine learning, do you foresee more governance coming into play in terms of what AI is allowed to do and the decisions of what training data is allowed to be fed to Ais in terms of influence? You talk about data determining if AI will become good or bad, but humans being the ones responsible for the training in the first place, obviously, they can use that data to influence as they, just the governance and the influence. >> Jack: Who wants to take that one? >> I'll take a quick stab at it. So, yes, it's going to be an open discussion. It's going to have to take place, because really, they're just machines. It's machine learning. We teach it. We teach it what to do, how to act. It's just an extension of us and in fact, I think you had a really great conversation or a statement at lunch where you talked about your product being an extension of a designer because, and we can get into that a little bit, but really, it's just going to do what we tell it to do. So there's definitely going to have to be discussions about what type of data we feed. It's all going to be centered around the use case and what that solves the use case. But I imagine that that will be a topic of discussion for a long time about what we're going to decide to do. >> Jack: Michelle do you want to comment on this thought of taking a designer's brain and putting it into a model somehow? >> Well, actually, what I wanted to say was that I think that the regulation and the governance around it is going to be self imposed by the the developer and data science community first, because I feel like even experts who have been doing this for a long time don't rally have their arms fully around what we're dealing with here. And so to expect our senators, our congressmen, women, to actually make regulation around it is a lot, because they're not technologists by training. They have a lot of other stuff going on. If the community that's already doing the work doesn't quite know what we're dealing with, then how can we expect them to get there? So I feel like that's going to be a long way off, but I think that the people who touch and feel and deal with models and with data sets and stuff everyday are the kind of people who are going to get together and self-regulate for a while, if they're good hearted people. And we talk about AI for good. Some people are bad. Those people won't respect those convenance that we come up with, but I think that's the place we have to start. >> So really you're saying, I think, for data scientists and those of us working in this space, we have a social, ethical, or moral obligation to humanity to ensure that our work is used for good. >> Michelle: No pressure. (laughing) >> None taken. Any other questions? Anything else? >> I just wanted to talk about the second part of what she said. We've been working with a company that builds robots for the store, a store associate if you will. And one of their very interesting findings was that the greatest acceptance of it right now has been at car dealerships because when someone goes to the car dealer and we all have had terrible experiences doing that. That's why we try to buy it online, but just this perception that a robot would be unbiased, that it will give you the information without trying to push me one way or the other. >> The hard sell. >> So there's that perception side of it too that, it isn't that the governance part of your question, but more the biased perception side of what you said. I think it's fascinating how we're already trained to think that this is going to have an unbiased opinion, whether or not that true. >> That's fascinating. Very cool. Thank you Sarush. Any other questions in the audience? No, okay. Michelle, could I ask, you've got a station over there that talks a little bit more about your company, but for those that haven't seen it yet, could you tell us a little bit about what is the experience like or how is the shopping experience different for someone that's using your company's technology than what it was before? >> Oh, free advertising. I would love to. No, but actually, I started this company because as a consumer I found myself going back to the user experience piece, just constantly frustrated with the user experience of buying products one at a time and then getting zero help. And then here I am having to google how to wear a white blazer to not look like an idiot in the morning when I get dressed with my white blazer that I just bought and I was excited about. And it's a really simple thing, which is how do I use the product that I'm buying and that really simple thing has been just abysmally handled in the retail industry, because the only tool that the retailers have right now are manual. So in fashion, some of our fashion customers like John Varvatos is an example we have over there, it's like a designer for high-end men's clothing, and John Varvatos is a person, it's not just the name of the company. He's an actual person and he has a vision for what he wants his products to look like and the aesthetic and the style and there's a rockstar vibe and to get that information into the organization, he would share it verbally with PDFs, thing like that. And then his team of merchandisers would literally go manually and make outfits on one page and then go make an outfit on another page with the same exact items and then products would go out of stock and they'd go around in circles and that's a terrible, terrible job. So to the conversation earlier about people losing jobs because of artificial intelligence. I hope people do lose jobs and I hope they're the terrible jobs that no one wanted to do in the first place, because the merchandisers that we help, like the one form John Varvatos, literally said she was weeks away from quitting and she got a new boss and said, "If you don't ix this part of my job, I'm out of here." And he had heard about us. He knew about us and so he brought us in to solve that problem. So I don't think it's always a bad thing, because if we can take that route, boring, repetitive task off of human's plates, what more amazing things can we do with our brain that is only human and very unique to us and how much more can we advance ourselves and our society by giving the boring work to a robot or a machine. >> Well, that's fantastic. So Joe, when you talk about Smart Cities, it seems like people have been talking about Smart Cities for decades and often people cite funding issues, regulatory environment or a host of other reasons why these things haven't happened. Do you think we're on the cusp of breaking through there or what challenges still remain for fulfilling that vision of a smart city? >> I do, I do think we're on the cusp. I think a lot of it has to do, largely actually, with 5G and connectivity, the ability to process and send all this data that needs to be shared across the system. I also think that we're getting closer and more conscientious about security, which is a major issue with IOT, making sure that our in devices or our edge devices, those things out there sensing, are secure. And I think interocular ability is something that we need to champion as well and make sure that we basically work together to enable these systems. So very, very difficult to create little, tiny walled gardens of solutions in a smart city. You may corner a certain part of the market, but you're definitely not going to have that ubiquitous benefit to society if you establish those little walled gardens, so those are the areas I think we need to focus on and I think we are making serious progress in all of them. >> Very good. Michelle, you mentioned earlier that artificial intelligence was all around us in lots of places and things that we do on a daily basis, but we probably don't realize it. Could you share a couple examples? >> Yeah, so I think everything you do online for the most part, literally anything you might do, whether that's googling something or you go to some article, the ads might be dynamically picked for you using machine learning models that have decided what is appropriate based on you and your treasure trove of data that you have out there that you're giving up all the time and not really understanding you're giving up >> The shoes that follow you around the internet right? >> Yeah, exactly. So that's basically anything online. I'm trying to give in the real-world. I think that, to your point earlier about he supply chain, just picking a box of cereal off the shelf and taking it home, there's not artificial intelligence in that at all, but the supply chain behind it. So the supply chain behind pretty much everything we do even in television, like how media gets to us and get consumed. At some point in the supply chain, there's artificial intelligence playing in there as well. >> So to start us in the supply chain where we can get the same day even within the hour delivery. How do you get better than that? What's coming that's innovative in the supply chain that will be new in the future? >> Well, so that is one example of it, but you'd be surprised at how inefficient the supply chain is, even with all the advances that have already gone in, whether it's physical advances around building modern warehouses and modern manufacturing plants, whether it's through software and others that really help schedule things and optimize things. What has happened in the supply chain just given how they've evolved is they're very siloed, so a lot of times the manufacturing plant does things that the distribution folks do not know. The distribution folks do things that the transportation folks don't know and then the store folks know nothing other than when the trucks pulls up, that's the first time they find out about things. So where the great opportunity in my mind is, in the space that I'm in, is really the generation of data, the connection of data, and finally, deriving the smarts that really help us improve efficiency. There's huge opportunity there. And again, we don't know it because it's all invisible to us. >> Good. Let me pause and see if there's any questions in the audience. There, we got one there. >> Thank you. Hi guys, you alright? I just had a question about ethics and the teaching of ethics. As you were saying, we feed the artificial intelligence, whereas in a scenario which is probably a little bit more attuned to automated driving, in a car crash scenario between do we crash these two people or three people? I would be choosing two, whereas the scenario may be it's actually better to just crash the car and kill myself. That thought would never go through my mind, because I'm human. My rule number one is self preservation. So how do we teach the computer this sort of side of it? Is there actually the AI ethic going to be better than our own ethics? How do we start? >> Yeah, that's a great question. I think the opportunity is there as Michelle was talking earlier about maybe when you cross that chasm and you get this new singularity, maybe the AI ethics will be better than human ethics because the machine will be able to think about greater concerns perhaps other than ourselves. But I think just from my point of view, working in the space of automated vehicles, I think it is going to have to be something that the industry, and societies are different, different geographies, and different countries. We have different ways of looking at the world. Cultures value different things and so I think technologists in those spaces are going to have to get together and agree amongst the community from a social contract theory standpoint perhaps in a way that's going to be acceptable to everyone who lives in that environment. I don't think we can come up with a uniform model that would apply to all spaces, but it's got to be something though that we all, as members of a community, can accept. And so yeah, that would be the right thing to do in that situation and that's not going to be an easy task by any means, which is, I think, one of the reasons why you'll continue to see humans have an important role to play in automated vehicles so that the human could take over in exactly that kind of scenario, because the machines perhaps aren't quite smart enough to do it or maybe it's not the smarts or the processing capability. It's maybe that we haven't as technologists and ethicists gotten together long enough to figure out what are those moral and ethical frameworks that we could use to apply to those situations. Any other thoughts? >> Yeah, I wanted to jump in there real quick. Absolutely questions that need to be answered, but let's come together and make a solution that needs to have those questions answered. So let's come together first and fix the problems that need to be fixed now so that we can build out those types of scenarios. We can now put our brainpower to work to decide what to do next. There was a quote I believe by Andrew Ningh Bidou and he was saying in concerning deep questions about what's going to happen in the future with AI. Are we going to have AI overlords or anything like that? And it's kind of like worrying about overpopulation at the point of Mars. Because maybe we're going to get there someday and maybe we're going to send people there and maybe we're going to establish a human population on Mars and then maybe it will get too big and then maybe we'll have problems on Mars, but right now we haven't landed on the planet and I thought that really does a good job of putting in perspective that that overall concern about AI taking over. >> So when you think about AI being applied for good and Michelle you talked about don't do AI just for AI's sake, have a problem to solve, I'll open it up to any of the three of you, what's a problem in your life or in your work experience that you'd love somebody out here would go solve with AI? >> I have one. Sorry, I wanted to do this real quick. There's roads blocked off and it's raining and I have to walk a mile to find a taxi in the rain right now after this to go home. I would love for us to have some sort of ability to manage parking spaces and determine when and who can come in to which parts of the city and when there's a spot downtown, I want my autonomous vehicle to know which one's available and go directly to that spot and I want it to be cued in a certain manner to where I'm next in line and I know. And so I would love for someone to go solve that problem. There's been some development on the infrastructure side for that kind of solution. We have a partnership Intel does with GE and we're putting sensors that have, it's an IOT sensor basically. It's called City IQ. It has environmental monitoring, audio, visual sensors and it allows this type of use case to take place. So I would love to see iterations on that. I would love to see, sorry there's another one that I'm particular about. Growing up I lived in Southern California right against the hills, a housing development, because the hills and there was not a factory, but a bunch of oil derricks back there. I would love to have sensor that senses the particulate in the air to see if there was too many fumes coming from that oil field into my yard growing up as a little kid. I would love for us to solve problems like that, so that's the type of thing that we'll be able to solve. Those are the types of innovations that will be able to take place once we have these sensors in place, so I'm going to sit down on that one and let someone else take over. >> I'm really glad you said the second one because I was thinking, "What I'm about to say is totally going to "trivialize Joe's pain and I don't want to do that." But cancer is my answer, because there's so much data in health and all these patterns are there waiting to be recognized. There's so many things you don't know about cancer and so many indicators that we could capture if we just were able to unmask the data and take a look, but I knew a brilliant company that was using artificial intelligence specifically around image processing to look at CAT scans and figure out what the leading indicators might be in a cancerous scenario. And they pivoted to some way more trivial problem which is still a problem and not to trivialize parking an whatnot, but it's not cancer. And they pivoted away from this amazing opportunity because of the privacy and the issues with HIPPA around health data. And I understand there's a ton of concern with it getting into the wrong hands and hacking and all of this stuff. I get that, but the opportunity in my mind far outweighs the risk and the fact that they had to change their business model and change their company essentially broke my heart because they were really onto something. >> Yeah that's a shame and it's funny you mention that. Intel has an effort that we're calling the cancer cloud and what we're trying to do is provide some infrastructure to help with that problem and the way cancer treatments work today is if you go to a university hospital let's say here in Texas, how you interpret that scan and how you respond and apply treatment, that knowledge is basically just kept within that hospital and within that staff. And so on the other side of the country, somebody could go in and get a scan and maybe that scan brand new to that facility and so they don't know how to treat it, but if you had an opportunity with machine learning to be able to compare scans from people, not only just in this country, but around the world and understand globally, all of the hundreds of different treatment pads that were applied to that particular kind of cancer, think how many lives could be saved, because then you're sharing knowledge with what courses of treatment worked. But it's one of those things like you say, sometimes it's the regulatory environment or it's other factors that hold us back from applying this technology to do some really good things, so it's a great example. Okay, any other questions in the audience? >> I have one. >> Good Emily. >> So this goes off of the HIPPA question, which is, and you were talking about just dynamically displaying ads earlier. What does privacy look like in a fully autonomous world? Anybody can answer that one. Are we still private citizens? What does it look like? >> How about from a supply chain standpoint? You can learn a lot about somebody in terms of the products that they buy and I think to all of us, we sort of know maybe somebody's tracking what we're buying but it's still creepy when we think about how people could potentially use that against us. So, how do you from a supply chain standpoint approach that problem? >> Yeah and it's something that comes up in my life almost every day because one of the thing's we'd like to do is to understand consumer behavior. How often am I buying? What kinds of products am I buying? What am I returning? And so for that you need transactional data. You really get to understand the individual. That then starts to get into this area of privacy. Do you know too much about me? And so a lot of times what we do is data is clearly anonymized so all we know is customer A has this tendency, customer B has this tendency. And that then helps the retailers offer the right products to these customers, but to your point, there are those privacy concerns and I think issues around governance, issues around ethics, issues around privacy, these will continue to be ironed out. I don't think there's a solid answer for any of these just yet. >> And it's largely a reflection of society. How comfortable are we with how much privacy? Right now I believe we put the individual in control of as much information as possible that they are able to release or not. And so a lot of what you said, everyone's anonymizing everything at the moment, but that may change as society's values change slightly and we'll be able to adapt to what's necessary. >> Why don't we try to stump the panel. Anyone have any ideas on things in your life you'd like to be solved with AI for good? Any suggestions out there that we could then hear from our data scientist and technologist and folks here? Any ideas? No? Alright good. Alright, well, thank you everyone. Really appreciate your time. Thank you for joining Intel here at the AI lounge at Autonomous World. We hope you've enjoyed the panel and we wish you a great rest of your event here at South by Southwest. (audience clapping) (bright music)

>> live from Boston, Massachusetts. It's the Cube covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now here are your hosts. Day villain Day >> and stew Minimum. We're back. Welcome to Boston, Everybody. This is the IBM Chief Data Officer Summit. This is the Cube, the worldwide leader in live tech coverage. Inderpal. Bhandari is here. He's the newly appointed chief data officer at IBM. He's joined, but joined by Bob Picciano who is the senior vice president of IBM Analytics Group. Bob. Great to see again Inderpal. Welcome. Thank you. Thank you. So good event, Bob, Let's start with you. Um, you guys have been on the chief data officer kicked for several years now. You ahead of the curve. What, are you trying to achieve it? That this event? Yes. So, >> Dave, thanks again for having us here. And thanks for being here is well, tto help your audience share in what we're doing here. We've always appreciated that your commitment to help in the the masses understand all the important pulses that are going on the industry. What we're doing here is we're really moderating form between chief date officers on. We started this really on the curve. As you said 2014, where the conference was pretty small, there were some people who were actually examining the role, thinking about becoming a chief did officer. We probably had a few formal cheap date officers we're talking about, you know, maybe 100 or so people who are participating in the very 1st 1 Now you can see it's not, You know, it's it's grown much larger. We have hundreds of people, and we're doing it multiple times a year in multiple cities. But what we're really doing is bringing together a moderated form, Um, and it's a privilege to be able to do this. Uh, this is not about selling anything to anybody. This is about exchanging ideas, understanding. You know what, the challenges of the role of the opportunities which changing about the role, what's changing about the market and the landscape, what new risks might be on the horizon? What new opportunities might be on the horizon on we you know, we really liketo listen very closely to what's going on so we can, you know, maybe build better approach is to help their mother. That's through the services we provide or whether that's through the cloud capabilities were offering or whether that's new products and services that need to be developed. And so it gives us a great understanding. And we're really fortunate to have our chief data officer here, Interpol, who's doing a great job in IBM and in helping us on our mission around really becoming a cognitive enterprise and making analytics and insight on data really be central to that transformation. >> So, Dr Bhandari, new, uh, new to the chief date officer role, not nude. IBM. You worked here and came back. I was first exposed to roll maybe 45 years ago with the chief Data officer event. OK, so you come in is the chief data officer in December. Where do you start? >> So, you know, I've had the fortune of being in this role for a long time. I was one of the earliest created, the role for healthcare in two thousand six. Then I have honed that roll over three different Steve Data officer appointments at health care companies. And now I'm at IBM. So I do have, you know, I do view with the job as a craft. So it's a practitioner job and there's a craft to it. And do I answer your question? There are five things that you have to do to get moving on the job, and three of those have to be non sequentially and to must be done and powerful but everything else. So the five alarm. The first thing is you've got to develop a data strategy and data strategy is around, is focused around having an understanding ofthe how the company monetize is or plans to monetize itself. You know, what is the strategic monetization part of the company? Not so much how it monetize is data. But what is it trying to do? How is it going to make money in the future? So in the case of IBM, it's all around cognition. It's around enabling customers to become cognitive businesses. So my data strategy or our data strategy, I should say, is focused on enabling cognition becoming a cauldron of enterprise. You know, we've now realized that impacto prerequisite for cognition. So that's the data strategy piece. And that's the very first thing that needs to be done because once you understand that, then you understand what data is critical for the company, so you don't boil the ocean instead, what you do is you begin to govern exactly what's necessary and make sure it's fit for purpose. And then you can also create trusted data sources around those critical data assets that are critical for the for the monetization strategy of the company's. Those three have to go in sequence because if you don't know what you can do to adequately kind of three, and they're also significant pitfalls if you don't follow that sequence because you can end up pointing the ocean and the other two activities that must be done concurrently. One is in terms ofthe establishing deep partnerships with the other areas of the company the key business units, the key functional units because that's how you end up understanding what that data strategy ought to be. You know, if you don't have that knowledge of the company by making that effort that due diligence, that it's very difficult to get the data strategy right, so you've got to establish those partnerships and then the 5th 1 is because this is a space where you do require very significant talent. You have to start developing that talent and that all the organizational capability right from day one. >> So, Bob, you said that, uh, data is the new middle manager. You can't have an effective middle manager come unless you at least have some framework that was just described. >> Yeah, absolutely. So, you know, when Interpol talks about that fourth initiative about the engagement with the business units and making sure that we're in alignment on how the company's monetizing its value to its clients, his involvement with our team goes way beyond how he thinks about what date it is that we're collecting in the products that you're offering and what we might understand about our customers or about the marketplace. His involvement goes also into how we're curating the right user experience for who we want to win power with our products and offerings. Sometimes that's the role of the chief date officer. Sometimes that's the role of a data engineer. Sometimes it's the role of a data scientist. You mentioned data becoming the new middle management middle manager. We think the citizen analyst is ushering in that from from their seat, But we also need to be able to, from a perspective, to help them eliminate the long tail and and get transparency, the information. And sometimes it's the application developer. So we, uh, we collaborate on a very frequent basis, where, when we think about offering new capabilities to those roles, well, what's the data implication of that? What's the governance implication of that? How do we make it a seamless experience? So as people start to move down the path of igniting all of the innovation across those roles, there is a continuum to the information to using To be able to do that, how it's serving the enterprise, how it leads to that transformation to be a cognitive enterprise on DH. That's a very, very close collaboration >> we're moving from. You said you talked the process era to what I just inserted to an insight era. Yeah, um, and I have a question around that I'm not sure exactly how to formulate it, but maybe you can help. In the process, era technology was unknown. The process was very well, Don't know. Well known, but technology was mysterious. But with IBM and said help today it seems as though process is unknown. The technology's pretty known look at what uber airbnb you're doing the grabbing different technologies and putting them together. But the process is his new first of all, is that a reasonable observation? And if so, what does that mean for chief data officers? >> So the process is, you know, is new in the sense that in terms ofthe making it a cognitive process, it's going to end up being new, right? So the memorization that you >> never done it before, but it's never been done before, right >> in that sense. But it's different from process automation in the past. This is much more about knowledge, being able to scale knowledge, not just, you know, across one process, but across all the process cities that make up a company. And so in there. That goes also to the comment about data being the middle manager. I mean, if you've essentially got the ability to scale and manage knowledge, not just data but knowledge in terms of the insights that the people who are working these processes are coming up in conjunction with these data and intelligent capabilities, that that that that that of the hub right, it's the intelligence system that's had the Hubble this that's enabling all that so that That's really what leads Teo leads to the so called civilization >> way had dates to another >> important aspect of this is the process is dramatically different in the sense that it's ongoing. It's it's continuous, right, the process and your intimacy with uber and the trust that you're developing. A brand doesn't start and stop with one transaction and actually, you know branches into many different things. So your expectations, a CZ that relationships have all changed. So what they need to understand about you, what they need to protect about you, how they need to protect you in their transformation, the richness of their service needs to continue to evolve. So how they perform that task on the abundance of information they have available to perform that task. But the difficulty of being able to really consume it and make use of it is is a change. The other thing is, it's a lot more conversational, right? So the process isn't a deterministic set of steps that someone at a desk can really formulate in a business rule or a static process. It's conversationally changes. It needs to be dis ambiguity, and it needs to introduce new information during the process of disintegration. And that really, really calls upon the capabilities of a cognitive system that is rich and its ability to understand and interact with natural language to potentially introduce other sources of rich information. Because you might take a picture about what you're experiencing and all those things change that that notion from process to the conversational element. >> Dr. Bhandari, you've got an interesting role. Companies like IBM I think about the Theo with the CDO. Not only do you have your internal role, but you're also you know, a model for people going out there. You come too. Events like this. You're trying to help people in the role you've been a CDO. It's, um, health care organization to tell Yu know what's different about being kind of internal role of IBM. What kind of things? IBM Obviously, you know, strong technology culture, But tell us a little bit inside. You've learned what anything surprise you. You know, in your time that you've been doing it. >> Oh, you know, over the course ofthe time that I've been doing the roll across four different organizations, >> I guess specifically at IBM. But what's different there? >> You know, I mean IBM, for one thing, is a the The environment has tremendous scale. And if you're essentially talking about taking cognition to the enterprise, that gives us a tremendous A desperate to try out all the capabilities that were basically offering to our to our customers and to home that in the context of our own enterprise, you know, to build our own cognitive enterprise. And that's the journey that way, sharing with our with our customers and so forth. So that's that's different in in in in it. That wasn't the case in the previous previous rules that I had. And I think the other aspect that's different is the complexity of the organisation. This is a large global organization that wasn't true off the previous roles as well. They were Muchmore, not America century, you know, organizations. And so there's a There's an aspect there that also then that's complexity of the role in terms ofthe having to deal with different countries, different languages, different regulations, it just becomes much more complex. >> You first became a CDO in two thousand six, You said two thousand six, which was the same year as the Federal Rules of Civil Procedure came out and the emails became smoking guns. And then it was data viewed as a liability, and now it's completely viewed as an asset. But traditionally the CDO role was financial services and health care and government and highly regulated businesses. And it's clearly now seeping into new industries. What's driving that? Is that that value? >> Well, it is. I mean, it's, I think, that understanding that. You know, there's a tremendous natural resource in in the information in the data. But there is, you know, very much you know, union Yang around that notion of being responsible. I mean, one of the things that we're very proud of is the type of trust that we established over 105 year journey with our clients in the types of interactions we have with one another, the level of intimacy that we have in their business and very foundation away, that we serve them on. So we can never, ever do anything to compromise that you know. So the focus on really providing the ability to do the necessary governance and to do the necessary data providence and lineage in cyber security while not stifling innovation and being able to push into the next horizon. Interpol mentioned the fact that IBM, in and of itself, we think of ourselves as a laboratory, a laboratory for cognitive information innovation, a laboratory for design and innovation, which is so necessary in the digital era. And I think we've done a really good job in the spaces, but we're constantly pushing the envelope. A good example of that is blockchain, a technology that you know sometimes people think about and nefarious circumstances about, You know, what it meant to the ability to launch a Silk Road or something of that nature. We looked at the innovation understanding quite a lot about it being one of the core interview innovators around it, and saw great promise in being able to transform the way people thought about, you know, clearing multiparty transactions and applied it to our own IBM credit organization To think about a very transparent hyper ledger, we could bring those multiple parties together. People could have transparency and the transactions have a great deal of access into that space, and in a very, very rapid amount of time, we're able to take our very sizable IBM credit organization and implement that hyper ledger. Also, while thinking about the data regulation, the data government's implications. I think that's a really >> That's absolutely right. I mean, I think you know, Bob mentioned the example about the IBM credit organizer Asian, but there is. There are implications far beyond that. Their applications far beyond that in the data space. You know, it affords us now the opportunity to bring together identity management. You know, the profiles that people create from data of security aspects and essentially combined all of these aspects into what will then really become a trusted source ofthe data. You know, by trusted by me, I don't mean internally, but trusted by the consumers off the data. The subject's off the data because you'll be able to do that much in a way that's absolutely appropriate, not just fit for business purpose, but also very, very respectful of the consent on DH. Those aspects the privacy aspect ofthe data. So Blockchain really is a critical technology. >> Hype alleges a great example. We're IBM edge this week. >> You're gonna be a world of Watson. >> We will be a world Watson. We had the CEO of ever ledger on and they basically brought 1,000,000 diamonds and bringing transparency for the diamond industry. It's it's fraught with, with fraud and theft and counterfeiting and >> helping preserve integrity, the industry and eliminating the blood diamonds. And they right. >> It's fascinating to see how you know this bitcoin. You know, when so many people disparaged it is a currency, but not just the currency. You know, you guys IBM saw that early on and obviously participated in the open source. Be, You know, the old saying follow the money with us is like follow the data. So if I understand correctly, your job, a CDO is to sort of super charge of the business lines with the data strategy. And then, Bob, you're job is the line of business managers the supercharge your customers, businesses with the data strategy. Is that right? Is that the right value >> chain? I think you nailed it. Yeah, that's >> one of the things people are struggling with these days is, you know, if they can get their own data in house, then they've also gotta deal with third party. That industry did everything like that. IBM's role in that data chain is really interesting. You talked this morning about kind of the Weather Channel and kind of the data play there. Yeah, you know what? What's IBM is rolling. They're going forward. >> It's one of the most exciting things. I think about how we've evolved our strategy. And, you know, we're very fortunate to have Jimmy at the helm. Who really understands, You know, that transformational landscape on DH, how partnerships really change the ability to innovate for the companies we serve on? It was very obvious in understanding our client's problems that while they had a wealth of information that we were dealing with internally, there was great promise and being able to introduce these outside signals. If you will insights from other sources of data, Sometimes I call them vectors of information that could really transform the way they were thinking about solving their customer problem. So, you know, why wouldn't you ever want to understand that customers sentiment about your brand or about the product or service? And as a consequence to that, you know, capabilities that are there on Twitter or we chat or line are essential to that, depending on where your brand is operating in your branch, probably operating in a multinational space anyway, so you have to listen to all those signals and they're all in multiple language and sentiment is very, very bespoke. It's a different language, so you have to apply sophisticated machine learning. We've invented new algorithms to understand how to glean the signal at all that white noise. You use the weather example as well. You know, we think about the economic impact of climate atmosphere, whether on business and its profound. It's 1/2 trillion dollars, you know, in each calendar year that are, you know, lost information, lost assets, lost opportunity, misplaced inventory, you know, un delivered inventory. And we think we can do a better job of helping our clients take the weather excuses out of business in a variety of different industries. And so we've focused our initiatives on that information integration, governance, understanding new analytics toe to introduce those outside signals directly in the heart and want to place it on the desk of the chief data officer of those who are innovating around information and data. >> My my joke last Columbus. If they was Dell's buying DMC, IBM is buying the weather company. What does What does that say? My question is Interpol. When when Emma happens. And Bob, when you go out and purchase companies that are data driven, what role does the chief data officer play in both em in a pre and post. >> So, you know, I think the one that there being a cop, just gonna touch on a couple of points that Bob Major and I'll address your question directly as well. Uh, in terms of the role of the chief data officer, I think you're giving me that question before how that's he walled. The one very interesting thing that's happening now with what IBM is doing is previously the chief data officer. All at least with regard to the data, Not so much the strategy, but the data itself was internal focused. You know, you kind of worried about the data you had in house or the data you're bringing in now you've gotta worry as much about the exogenous status and because, you know, that's so That's one way that that role has changed considerably and is changing and evolving, and it's creating new opportunities for us. The other is again. In the past, the chief state officer all was around creating a warehouse for analytics and separated out from the operational processes. That's changing, too, because now we've got to transform these processes themselves. So that's, you know, that's that's another expanded role to come back to. Acquisitions emanate. I mean, I view that as essentially another process that, you know, company has. And so the chief data officer role is pretty key in terms of enabling that world in terms ofthe data, but also in terms ofthe giving, you know, guidance and advice. If, for instance, the acquisition isn't that problem itself, then you know, then we would be more closely involved. But if it's beyond that in terms of being able to get the right data, do that process as well as then once you've acquired the company in being able to integrate back the critical data assets those out of the key aspect, it's an ongoing role. >> So you've got the simplest level. You've got data sources and all the things associated with that. And then you've got your algorithms and your machine learning, and we're moving beyond sort of do tow cut costs into this new era. But so hot Oh cos adjudicate. And I guess you got to do both. You've got to get new data sources and you've got to improve this continuous process. By that you talked about how do you guide your customers as to where they put their resource? No. And that's >> really Davis. You have, you know, touching out again. That's really the benefit of this sort of a forum. In this sort of a conference, it's sharing the best practices of how the top experts in the world are really wrestling with that and identifying. I think you know Interpol's framework. What do you do sequentially to build the disciplines, to build a solid corn foundation, to make the connections that are lined with the business strategy? And then what do you do concurrently along that model to continue to operate? And how do you How do you manage and make sure your stakeholders understand what's being done? What they need to continue to do to evolve the innovation and come join us here and we'll go through that in detail. But, you know, he deposited a greatjob sharing his framers of success, and I think in the other room, other CEOs are doing that now. >> Yeah, I just wanted to quickly add to Bob's comment. The framework that I described right? It has a check and balance built into it because if you are all about governance, then the Sirio role becomes very defensive in nature. It's all about making sure you within the hour, you know, within the guard rails and so forth. But you're not really moving forward in a strategic way to help the company. And and that's why you know, setting it up by driving it from the strategy don't just makes it easier to strike that plus >> clerical and more about innovation here. We talked about the D and CDO today meaning data, but really, I think about it is being a great crucible for for disruption in information because you've disruption off. I called the Chief Disruption Office under Sheriff you >> incident in Data's digitalis data. So there's that piece of Ava's Well, we have to go. I don't want to go. So that way one last question for each of you. So Interpol, uh, thinking about and you just kind of just touched on it. He's not just playing defense, you know, thinking more offense this role. Where do you want to take it. What do your you know, sort of mid term, long term goals with this role? >> It's the specific role in IBM or just in general specifically. Well, I think in the case of I B M, we have the data strategy pretty well defined. Now it's all about being able to enable a cognitive enterprise. And so in, You know, in my mind and 2 to 3 years, we'll have completely established how that ought to be done, you know, as a prescription. And we'll also have our clients essentially sharing in that in that journey so that they can go off and create cognitive enterprises themselves. So that's pretty well set. You know, I have a pretty short window to three years to make that make that happen, And I think it's it's doable. And I think it will be, you know, just just a tremendous transformation. >> Well, we're excited to be to be watching and documenting that Bob, I have to ask you a world of washing coming up. New name for new conference. We're trying to get Pepper on, trying to get Jimmy on. Say, what should we expect? Maybe could. Although it was >> coming, and I think this year we're sort of blowing the roof off on literally were getting so big that we had to move the venue. It is very much still in its core that multiple practitioner, that multiple industry event that you experienced with insight, right? So whether or not you're thinking about this and the auspices of managing your traditional environments and what you need to do to bring them into the future and how you tie these things together, that's there for you. All those great industry tracks around the product agendas and what's coming out are are there. But the level of inspiration and involvement around this cognitive innovation space is going to be front and center. We're joined by Ginny Rometty herself, who's going to be very special. Key note. We have, I think, an unprecedented lineup of industry leaders who were going to come and talk about disruption and about disruption in the cognitive era on then. And as always, the most valuable thing is the journeys that our clients are partners sharing with us about how we're leading this inflection point transformation, the industry. So I'm very much excited to see their and I hope that your audience joins us as well. >> Great. We'll Interpol. Congratulations on the new roll. Thank you. Get a couple could plug, block post out of your comments today, so I really appreciate that, Bob. Always a pleasure. Thanks so much for having us here. Really? Appreciate. >> Thanks for having us. >> Alright. Keep right, everybody, this is the Cube will be back. This is the IBM Chief Data Officer Summit. We're live from Boston. You're back. My name is Dave Volante on DH. I'm along.