(upbeat music) >> Announcer: It's theCUBE covering the virtual Vertica Big Data Conference 2020 brought to you by Vertica. >> Welcome back to the Vertica Virtual Big Data Conference, BDC 2020. You know, it was supposed to be a physical event in Boston at the Encore. Vertica pivoted to a digital event, and we're pleased that The Cube could participate because we've participated in every BDC since the inception. Rich Gaston this year is the global solutions architect for security risk and governance at Micro Focus. Rich, thanks for coming on, good to see you. >> Hey, thank you very much for having me. >> So you got a chewy title, man. You got a lot of stuff, a lot of hairy things in there. But maybe you can talk about your role as an architect in those spaces. >> Sure, absolutely. We handle a lot of different requests from the global 2000 type of organization that will try to move various business processes, various application systems, databases, into new realms. Whether they're looking at opening up new business opportunities, whether they're looking at sharing data with partners securely, they might be migrating it to cloud applications, and doing migration into a Hybrid IT architecture. So we will take those large organizations and their existing installed base of technical platforms and data, users, and try to chart a course to the future, using Micro Focus technologies, but also partnering with other third parties out there in the ecosystem. So we have large, solid relationships with the big cloud vendors, with also a lot of the big database spenders. Vertica's our in-house solution for big data and analytics, and we are one of the first integrated data security solutions with Vertica. We've had great success out in the customer base with Vertica as organizations have tried to add another layer of security around their data. So what we will try to emphasize is an enterprise wide data security approach, where you're taking a look at data as it flows throughout the enterprise from its inception, where it's created, where it's ingested, all the way through the utilization of that data. And then to the other uses where we might be doing shared analytics with third parties. How do we do that in a secure way that maintains regulatory compliance, and that also keeps our company safe against data breach. >> A lot has changed since the early days of big data, certainly since the inception of Vertica. You know, it used to be big data, everyone was rushing to figure it out. You had a lot of skunkworks going on, and it was just like, figure out data. And then as organizations began to figure it out, they realized, wow, who's governing this stuff? A lot of shadow IT was going on, and then the CIO was called to sort of reign that back in. As well, you know, with all kinds of whatever, fake news, the hacking of elections, and so forth, the sense of heightened security has gone up dramatically. So I wonder if you can talk about the changes that have occurred in the last several years, and how you guys are responding. >> You know, it's a great question, and it's been an amazing journey because I was walking down the street here in my hometown of San Francisco at Christmastime years ago and I got a call from my bank, and they said, we want to inform you your card has been breached by Target, a hack at Target Corporation and they got your card, and they also got your pin. And so you're going to need to get a new card, we're going to cancel this. Do you need some cash? I said, yeah, it's Christmastime so I need to do some shopping. And so they worked with me to make sure that I could get that cash, and then get the new card and the new pin. And being a professional in the inside of the industry, I really questioned, how did they get the pin? Tell me more about this. And they said, well, we don't know the details, but you know, I'm sure you'll find out. And in fact, we did find out a lot about that breach and what it did to Target. The impact that $250 million immediate impact, CIO gone, CEO gone. This was a big one in the industry, and it really woke a lot of people up to the different types of threats on the data that we're facing with our largest organizations. Not just financial data; medical data, personal data of all kinds. Flash forward to the Cambridge Analytica scandal that occurred where Facebook is handing off data, they're making a partnership agreement --think they can trust, and then that is misused. And who's going to end up paying the cost of that? Well, it's going to be Facebook at a tune of about five billion on that, plus some other finds that'll come along, and other costs that they're facing. So what we've seen over the course of the past several years has been an evolution from data breach making the headlines, and how do my customers come to us and say, help us neutralize the threat of this breach. Help us mitigate this risk, and manage this risk. What do we need to be doing, what are the best practices in the industry? Clearly what we're doing on the perimeter security, the application security and the platform security is not enough. We continue to have breaches, and we are the experts at that answer. The follow on fascinating piece has been the regulators jumping in now. First in Europe, but now we see California enacting a law just this year. They came into a place that is very stringent, and has a lot of deep protections that are really far-reaching around personal data of consumers. Look at jurisdictions like Australia, where fiduciary responsibility now goes to the Board of Directors. That's getting attention. For a regulated entity in Australia, if you're on the Board of Directors, you better have a plan for data security. And if there is a breach, you need to follow protocols, or you personally will be liable. And that is a sea change that we're seeing out in the industry. So we're getting a lot of attention on both, how do we neutralize the risk of breach, but also how can we use software tools to maintain and support our regulatory compliance efforts as we work with, say, the largest money center bank out of New York. I've watched their audit year after year, and it's gotten more and more stringent, more and more specific, tell me more about this aspect of data security, tell me more about encryption, tell me more about money management. The auditors are getting better. And we're supporting our customers in that journey to provide better security for the data, to provide a better operational environment for them to be able to roll new services out with confidence that they're not going to get breached. With that confidence, they're not going to have a regulatory compliance fine or a nightmare in the press. And these are the major drivers that help us with Vertica sell together into large organizations to say, let's add some defense in depth to your data. And that's really a key concept in the security field, this concept of defense in depth. We apply that to the data itself by changing the actual data element of Rich Gaston, I will change that name into Ciphertext, and that then yields a whole bunch of benefits throughout the organization as we deal with the lifecycle of that data. >> Okay, so a couple things I want to mention there. So first of all, totally board level topic, every board of directors should really have cyber and security as part of its agenda, and it does for the reasons that you mentioned. The other is, GDPR got it all started. I guess it was May 2018 that the penalties went into effect, and that just created a whole Domino effect. You mentioned California enacting its own laws, which, you know, in some cases are even more stringent. And you're seeing this all over the world. So I think one of the questions I have is, how do you approach all this variability? It seems to me, you can't just take a narrow approach. You have to have an end to end perspective on governance and risk and security, and the like. So are you able to do that? And if so, how so? >> Absolutely, I think one of the key areas in big data in particular, has been the concern that we have a schema, we have database tables, we have CALMS, and we have data, but we're not exactly sure what's in there. We have application developers that have been given sandbox space in our clusters, and what are they putting in there? So can we discover that data? We have those tools within Micro Focus to discover sensitive data within in your data stores, but we can also protect that data, and then we'll track it. And what we really find is that when you protect, let's say, five billion rows of a customer database, we can now know what is being done with that data on a very fine grain and granular basis, to say that this business process has a justified need to see the data in the clear, we're going to give them that authorization, they can decrypt the data. Secure data, my product, knows about that and tracks that, and can report on that and say at this date and time, Rich Gaston did the following thing to be able to pull data in the clear. And that could be then used to support the regulatory compliance responses and then audit to say, who really has access to this, and what really is that data? Then in GDPR, we're getting down into much more fine grained decisions around who can get access to the data, and who cannot. And organizations are scrambling. One of the funny conversations that I had a couple years ago as GDPR came into place was, it seemed a couple of customers were taking these sort of brute force approach of, we're going to move our analytics and all of our data to Europe, to European data centers because we believe that if we do this in the U.S., we're going to violate their law. But if we do it all in Europe, we'll be okay. And that simply was a short-term way of thinking about it. You really can't be moving your data around the globe to try to satisfy a particular jurisdiction. You have to apply the controls and the policies and put the software layers in place to make sure that anywhere that someone wants to get that data, that we have the ability to look at that transaction and say it is or is not authorized, and that we have a rock solid way of approaching that for audit and for compliance and risk management. And once you do that, then you really open up the organization to go back and use those tools the way they were meant to be used. We can use Vertica for AI, we can use Vertica for machine learning, and for all kinds of really cool use cases that are being done with IOT, with other kinds of cases that we're seeing that require data being managed at scale, but with security. And that's the challenge, I think, in the current era, is how do we do this in an elegant way? How do we do it in a way that's future proof when CCPA comes in? How can I lay this on as another layer of audit responsibility and control around my data so that I can satisfy those regulators as well as the folks over in Europe and Singapore and China and Turkey and Australia. It goes on and on. Each jurisdiction out there is now requiring audit. And like I mentioned, the audits are getting tougher. And if you read the news, the GDPR example I think is classic. They told us in 2016, it's coming. They told us in 2018, it's here. They're telling us in 2020, we're serious about this, and here's the finds, and you better be aware that we're coming to audit you. And when we audit you, we're going to be asking some tough questions. If you can't answer those in a timely manner, then you're going to be facing some serious consequences, and I think that's what's getting attention. >> Yeah, so the whole big data thing started with Hadoop, and Hadoop is open, it's distributed, and it just created a real governance challenge. I want to talk about your solutions in this space. Can you tell us more about Micro Focus voltage? I want to understand what it is, and then get into sort of how it works, and then I really want to understand how it's applied to Vertica. >> Yeah, absolutely, that's a great question. First of all, we were the originators of format preserving encryption, we developed some of the core basic research out of Stanford University that then became the company of Voltage; that build-a-brand name that we apply even though we're part of Micro Focus. So the lineage still goes back to Dr. Benet down at Stanford, one of my buddies there, and he's still at it doing amazing work in cryptography and keeping moving the industry forward, and the science forward of cryptography. It's a very deep science, and we all want to have it peer-reviewed, we all want to be attacked, we all want it to be proved secure, that we're not selling something to a major money center bank that is potentially risky because it's obscure and we're private. So we have an open standard. For six years, we worked with the Department of Commerce to get our standard approved by NIST; The National Institute of Science and Technology. They initially said, well, AES256 is going to be fine. And we said, well, it's fine for certain use cases, but for your database, you don't want to change your schema, you don't want to have this increase in storage costs. What we want is format preserving encryption. And what that does is turns my name, Rich, into a four-letter ciphertext. It can be reversed. The mathematics of that are fascinating, and really deep and amazing. But we really make that very simple for the end customer because we produce APIs. So these application programming interfaces can be accessed by applications in C or Java, C sharp, other languages. But they can also be accessed in Microservice Manor via rest and web service APIs. And that's the core of our technical platform. We have an appliance-based approach, so we take a secure data appliance, we'll put it on Prim, we'll make 50 of them if you're a big company like Verizon and you need to have these co-located around the globe, no problem; we can scale to the largest enterprise needs. But our typical customer will install several appliances and get going with a couple of environments like QA and Prod to be able to start getting encryption going inside their organization. Once the appliances are set up and installed, it takes just a couple of days of work for a typical technical staff to get done. Then you're up and running to be able to plug in the clients. Now what are the clients? Vertica's a huge one. Vertica's one of our most powerful client endpoints because you're able to now take that API, put it inside Vertica, it's all open on the internet. We can go and look at Vertica.com/secure data. You get all of our documentation on it. You understand how to use it very quickly. The APIs are super simple; they require three parameter inputs. It's a really basic approach to being able to protect and access data. And then it gets very deep from there because you have data like credit card numbers. Very different from a street address and we want to take a different approach to that. We have data like birthdate, and we want to be able to do analytics on dates. We have deep approaches on managing analytics on protected data like Date without having to put it in the clear. So we've maintained a lead in the industry in terms of being an innovator of the FF1 standard, what we call FF1 is format preserving encryption. We license that to others in the industry, per our NIST agreement. So we're the owner, we're the operator of it, and others use our technology. And we're the original founders of that, and so we continue to sort of lead the industry by adding additional capabilities on top of FF1 that really differentiate us from our competitors. Then you look at our API presence. We can definitely run as a dup, but we also run in open systems. We run on main frame, we run on mobile. So anywhere in the enterprise or one in the cloud, anywhere you want to be able to put secure data, and be able to access the protect data, we're going to be there and be able to support you there. >> Okay so, let's say I've talked to a lot of customers this week, and let's say I'm running in Eon mode. And I got some workload running in AWS, I've got some on Prim. I'm going to take an appliance or multiple appliances, I'm going to put it on Prim, but that will also secure my cloud workloads as part of a sort of shared responsibility model, for example? Or how does that work? >> No, that's absolutely correct. We're really flexible that we can run on Prim or in the cloud as far as our crypto engine, the key management is really hard stuff. Cryptography is really hard stuff, and we take care of all that, so we've all baked that in, and we can run that for you as a service either in the cloud or on Prim on your small Vms. So really the lightweight footprint for me running my infrastructure. When I look at the organization like you just described, it's a classic example of where we fit because we will be able to protect that data. Let's say you're ingesting it from a third party, or from an operational system, you have a website that collects customer data. Someone has now registered as a new customer, and they're going to do E-commerce with you. We'll take that data, and we'll protect it right at the point of capture. And we can now flow that through the organization and decrypt it at will on any platform that you have that you need us to be able to operate on. So let's say you wanted to pick that customer data from the operational transaction system, let's throw it into Eon, let's throw it into the cloud, let's do analytics there on that data, and we may need some decryption. We can place secure data wherever you want to be able to service that use case. In most cases, what you're doing is a simple, tiny little atomic efetch across a protected tunnel, your typical TLS pipe tunnel. And once that key is then cashed within our client, we maintain all that technology for you. You don't have to know about key management or dashing. We're good at that; that's our job. And then you'll be able to make those API calls to access or protect the data, and apply the authorization authentication controls that you need to be able to service your security requirements. So you might have third parties having access to your Vertica clusters. That is a special need, and we can have that ability to say employees can get X, and the third party can get Y, and that's a really interesting use case we're seeing for shared analytics in the internet now. >> Yeah for sure, so you can set the policy how we want. You know, I have to ask you, in a perfect world, I would encrypt everything. But part of the reason why people don't is because of performance concerns. Can you talk about, and you touched upon it I think recently with your sort of atomic access, but can you talk about, and I know it's Vertica, it's Ferrari, etc, but anything that slows it down, I'm going to be a concern. Are customers concerned about that? What are the performance implications of running encryption on Vertica? >> Great question there as well, and what we see is that we want to be able to apply scale where it's needed. And so if you look at ingest platforms that we find, Vertica is commonly connected up to something like Kafka. Maybe streamsets, maybe NiFi, there are a variety of different technologies that can route that data, pipe that data into Vertica at scale. Secured data is architected to go along with that architecture at the node or at the executor or at the lowest level operator level. And what I mean by that is that we don't have a bottleneck that everything has to go through one process or one box or one channel to be able to operate. We don't put an interceptor in between your data and coming and going. That's not our approach because those approaches are fragile and they're slow. So we typically want to focus on integrating our APIs natively within those pipeline processes that come into Vertica within the Vertica ingestion process itself, you can simply apply our protection when you do the copy command in Vertica. So really basic simple use case that everybody is typically familiar with in Vertica land; be able to copy the data and put it into Vertica, and you simply say protect as part of the data. So my first name is coming in as part of this ingestion. I'll simply put the protect keyword in the Syntax right in SQL; it's nothing other than just an extension SQL. Very very simple, the developer, easy to read, easy to write. And then you're going to provide the parameters that you need to say, oh the name is protected with this kind of a format. To differentiate it between a credit card number and an alphanumeric stream, for example. So once you do that, you then have the ability to decrypt. Now, on decrypt, let's look at a couple different use cases. First within Vertica, we might be doing select statements within Vertica, we might be doing all kinds of jobs within Vertica that just operate at the SQL layer. Again, just insert the word "access" into the Vertica select string and provide us with the data that you want to access, that's our word for decryption, that's our lingo. And we will then, at the Vertica level, harness the power of its CPU, its RAM, its horsepower at the node to be able to operate on that operator, the decryption request, if you will. So that gives us the speed and the ability to scale out. So if you start with two nodes of Vertica, we're going to operate at X number of hundreds of thousands of transactions a second, depending on what you're doing. Long strings are a little bit more intensive in terms of performance, but short strings like social security number are our sweet spot. So we operate very very high speed on that, and you won't notice the overhead with Vertica, perse, at the node level. When you scale Vertica up and you have 50 nodes, and you have large clusters of Vertica resources, then we scale with you. And we're not a bottleneck and at any particular point. Everybody's operating independently, but they're all copies of each other, all doing the same operation. Fetch a key, do the work, go to sleep. >> Yeah, you know, I think this is, a lot of the customers have said to us this week that one of the reasons why they like Vertica is it's very mature, it's been around, it's got a lot of functionality, and of course, you know, look, security, I understand is it's kind of table sticks, but it's also can be a differentiator. You know, big enterprises that you sell to, they're asking for security assessments, SOC 2 reports, penetration testing, and I think I'm hearing, with the partnership here, you're sort of passing those with flying colors. Are you able to make security a differentiator, or is it just sort of everybody's kind of got to have good security? What are your thoughts on that? >> Well, there's good security, and then there's great security. And what I found with one of my money center bank customers here in San Francisco was based here, was the concern around the insider access, when they had a large data store. And the concern that a DBA, a database administrator who has privilege to everything, could potentially exfil data out of the organization, and in one fell swoop, create havoc for them because of the amount of data that was present in that data store, and the sensitivity of that data in the data store. So when you put voltage encryption on top of Vertica, what you're doing now is that you're putting a layer in place that would prevent that kind of a breach. So you're looking at insider threats, you're looking at external threats, you're looking at also being able to pass your audit with flying colors. The audits are getting tougher. And when they say, tell me about your encryption, tell me about your authentication scheme, show me the access control list that says that this person can or cannot get access to something. They're asking tougher questions. That's where secure data can come in and give you that quick answer of it's encrypted at rest. It's encrypted and protected while it's in use, and we can show you exactly who's had access to that data because it's tracked via a different layer, a different appliance. And I would even draw the analogy, many of our customers use a device called a hardware security module, an HSM. Now, these are fairly expensive devices that are invented for military applications and adopted by banks. And now they're really spreading out, and people say, do I need an HSM? Well, with secure data, we certainly protect your crypto very very well. We have very very solid engineering. I'll stand on that any day of the week, but your auditor is going to want to ask a checkbox question. Do you have HSM? Yes or no. Because the auditor understands, it's another layer of protection. And it provides me another tamper evident layer of protection around your key management and your crypto. And we, as professionals in the industry, nod and say, that is worth it. That's an expensive option that you're going to add on, but your auditor's going to want it. If you're in financial services, you're dealing with PCI data, you're going to enjoy the checkbox that says, yes, I have HSMs and not get into some arcane conversation around, well no, but it's good enough. That's kind of the argument then conversation we get into when folks want to say, Vertica has great security, Vertica's fantastic on security. Why would I want secure data as well? It's another layer of protection, and it's defense in depth for you data. When you believe in that, when you take security really seriously, and you're really paranoid, like a person like myself, then you're going to invest in those kinds of solutions that get you best in-class results. >> So I'm hearing a data-centric approach to security. Security experts will tell you, you got to layer it. I often say, we live in a new world. The green used to just build a moat around the queen, but the queen, she's leaving her castle in this world of distributed data. Rich, incredibly knowlegable guest, and really appreciate you being on the front lines and sharing with us your knowledge about this important topic. So thanks for coming on theCUBE. >> Hey, thank you very much. >> You're welcome, and thanks for watching everybody. This is Dave Vellante for theCUBE, we're covering wall-to-wall coverage of the Virtual Vertica BDC, Big Data Conference. Remotely, digitally, thanks for watching. Keep it right there. We'll be right back right after this short break. 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>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with it's ecosystem partners. >> Welcome inside Live here at the Sands as we continue our coverage of AWS re:Invent 2019 on theCUBE, day three. Always an exciting time I think to get a summary of what's happened here. Dave Vellante, John Walls, we're joined by a couple of gentlemen from Immuta, Steven Touw who's a co-founder and CTO. Steve, good to see you. >> Yeah thanks for having me. >> John Walls: And Rob Lancaster, who's the GM of Cloud at Immuta. Rob, thanks for joining us as well. >> Great to be here. >> First off, let's talk about Immuta a little bit. You're all about governance right? You're trying to make it simple, easy, taking out the complexity. But for those at home who might not be too familiar with your company, tell us a little bit about you. >> Yeah so the company started out, our roots are in the U.S. intelligence community. So we had been dealing with access and control issues for data for years and we said to ourselves, "Hey this product has to be useful for non-IC customers. "This problem has to exist." And with the advent of all these privacy regulations like CCPA, GDPR and of course HIPPA's been around for a long time, really our goal was to bring a product to the market that makes it easy to govern access to data in a way that you don't have to be technical to do it, you don't have to understand how to write SQL statements, you don't have to be a system administrator. We really bring together three personas, the users that want to get access to the data, legal compliance that needs to understand how the rules are being enforced or even enforce them themselves, and then of course the data owners and the DBAs who need to expose the data. So usually those three personas are at odds with one another, we bring them together in our platform and allow them to work together in a way that's compliant and also accelerates their data analytics. >> Could we talk a little bit about why this is such a problem? Because it is a big problem and especially today and in the cloud and we'll get into that, but you've got data lakes, data oceans now, you got data coming in, all types of data. Might be internal transaction data, it might be stuff in your data warehouse. And the organization say, "Well I want some other data. "I want to bring in maybe some social data." So certain data is, everybody can have access to. Certain data not everybody can have access to. And it's not necessarily just a security problem, edicts of my organization that need to be enforced. So first of all, is that sort of, the problem that you're solving? And maybe you can double-click on that a little bit. >> Yeah sure, so the market has evolved and is evolving. You allude to data lakes, I think you can point to the immersion of Hadoop, as a distributed infrastructure as kind of the original data lakes, or the most recent data lakes, where you can store all your data and run analytics on all your data, and now with the advent, with the emergence of Cloud you've effectively got very low, if not zero cost storage, and the ability to throw an unlimited amount of compute at the data. That, kind of in conjunction with heightened awareness for consumer data privacy and risk associated with data, has created a market for data governance beyond kind of the course-grained access controls that people have been using on their databases for decades now. >> Yeah I mean Hadoop really got it all started. You're right and despite all it's problems, it had some real epiphany-like technical innovations, but one of the things that it didn't worry about at the time was governance. So whose responsibility is this? Is it the CISO? That is essentially trying to build out a new cloud stack to provide security, privacy, governance and what does that stack look like? >> Rob: Go ahead. >> Yeah so it depends, it's actually pretty interesting that different organizations have tackled this different ways. So we have CISOs that maintain this. In other organizations we've got the legal compliance teams that want to do this but maybe don't have the technical chops. And the CISO doesn't necessarily know all the privacy rules that need to be enforced, so it's kind of moving into this world where security is about keeping the bad guys out and black or white access, like you either can see the data or you won't, but with privacy controls it gets into this gray area where there's a lot of technical complexity and there's a lot of legal complexity. So the organizations struggle with this 'cause you've got to play in that gray area where it's not just like I said, black and white. The analogy we use is, security is like a light switch, you're either in or you're out. With privacy controls you need to anonymize the data, you need to do privacy by design. It's like a dimmer switch where you want to play in that gray area and allow some utility out of the data but also protect privacy at differing levels of whatever you're doing analytically. So this can be challenging for an organization to wrestle with because it's not as, I would argue it's not as black and white as it is with security. >> Your question is in many cases it's the business that's running really fast and that is building these data lakes because they want to get value out of their data and the CISO or the compliance or risk officers are the ones that are telling them to slow down. So our product that Steve set up caters to both parties. It checks the boxes for risk, but it also enable the business to get utility out of their data lake. >> It's a very complicated situation because you've got this corpus of data that's organic and constantly changing and you have, you mentioned GDPR, you've got California now, every state's going to have it's own regulations so you've got to be able to sort of adjudicate that. And can you talk about, I mean obviously I've interviewed Matt Carroll, we covered you guys so I know a little bit about you, but can you talk about your tech in terms of it's ability? You've got a capability to do really granular level understanding and governance policies, can you describe that a little bit? >> Yeah sure, so when we talk about privacy controls, these are things like way beyond just table-level access. So instead of saying, "Hey you have access to this table or not," or even, "You have access to this column or not," you've got to go deeper than that, you've got to be able to make rows disappear based on what people are doing. So for example, we have financial institution customers that are using us for all their trading data and only some traders can see some trade desks and we manage all that dynamically. We're not making anonymized copies of data. Everything happens at query time, and depending on what compute you're using that all works differently, but then at the column level we're able to do these anonymization techniques like we could make numeric data less specific, we could use techniques like k-anonymization that allows analysts to analyze the data but ensures that small groups that exist in that data won't reveal someone's true identity. And we have techniques like differential privacy, which provides mathematical guarantees of privacy. So for example, one of our manufacturing customers set aside, these are the four analytical use cases that we're using our data for and under GDPR we want different levels of privacy associated to those use cases. So they could do that all with Immuta. So they could say, "When I'm doing this "I want these columns to be anonymized to this level "and these rows to disappear, but if I'm doing something, "maybe more critical, which our consumers have consented to "you know there's less privacy controls." And that all happens dynamically so the analysts could actually switch context of what they're doing and get a different view of the data and all of that is audited so we understand why someone's doing what they're doing and when they're running queries we can associate those queries to purpose. >> We've talked about customers of course and they're adapting right, to a new world? How are you adapting? I mean what are you learning about, in terms of policy regulation and governance, what have you, you said you came out of the intelligence community, high bar there right? >> Steven Touw: Yeah. >> So what have you done to evolve as a company and what are you, as the headlights basically for these folks, what are you seeing change that is going to require a lot of shift on the other side? >> Yeah so, I don't know if you have thoughts. >> I mean it's a great question but there's really two parts to it, there's what are we doing? But, what is the market doing as well, right? So if you think about when we got started, even a year ago people understood the technology, they thought it was cool but maybe a little nichey for government or financial services or maybe healthcare because there's well understood regulation, these vertical regulation. Even over the past year with kind of this increasing or heightened awareness for consumer data privacy, not just driven by CCPA and GDPR but kind of this, call it the Facebook Effect right? Cambridge Analytica has created this awareness within the general population for what are these organizations actually doing with my data? Before it was okay 'cause you give your data to Google and you get a better search result and you're okay with that but now they may be using your data for their own profit in different ways so this has created this rising tides effect for the overall market and we talk a lot about organizations using something like Immuta to protect their highly sensitive data. I like to think of it is their most valuable data, which may be highly sensitive but it also could be the crown jewels, trading data for a bank for example. So it's become about extracting value and operational benefit from data, whereas the risk offices are trying to lock it down in many cases. >> So, there's definitely a big problem and people are becoming more aware of it. I want to talk about where you guys fit into this whole cloud ecosystem. There's a sea change now, there's this sort of, this new cloud coming into play. It's not just about infrastructure anymore. I'll give you some examples, you got all these data lakes, maybe you got Redshift running, Snowflake's another one, you've now got this data exchange where you can bring data right in the Cloud bring in all different types of data, you're bringing in some AML and AI and it's all, really again, a complicated situation. So I see you guys as fitting in there and real need but can you describe where you fit in the ecosystem, what your relationship is with AWS, how do I engage with you? >> Yeah absolutely, so a core part of our value is that we are heterogeneous in terms of the environment that we support. We support a hybrid estate so the architecture of the product is fully microservices based so we can run on PRIM as well as on Cloud, on any Cloud, we support effectively any popular database system or analytical tool. So think of us as a data abstraction layer across a hybrid environment, so we're here because AWS is obviously the big boy in the market, they have market share, this is a strategic relationship for us. We're working very deeply with AWS field teams, particularly around some of their verticals, the verticals that align to our business and at the end of the day we're trying to define a category. It's a similar category that we've had for decades but with all the changes that are happening in data and regulation and infrastructure what we're trying to do is raise the level of awareness for the fact that Immuta has actually solved the problem that many of these risk officers are struggling with today. >> Yeah and from a, diving a little on the technical side of that answer is that we are, think of us as the way to enforce policy in the Cloud. We consider ourselves a Cloud-first software vendor. And you don't necessarily want one point solution in Redshift or another point solution on your on-premise Cloudera instance, whatever it may be where you're using your data and running analytics, you need to abstract the policies out into a consistent layer and then have them be enforced across whatever you're using. So you might be using Cloudera today and then you switch to Databricks tomorrow, that shouldn't be a hard change from you from a policy perspective. You just re-point Immuta at Databricks and all your policies are still working like they used to so it gives you this flexibility now to use all these different services that AWS provides 'cause as was stated in the keynote on Tuesday, there's no one database solves all. You're always going to be using a heterogenous set of compute to do your job in analytics so you need a consistent way to enforce policies across all of that. >> That's a great point. I mean I don't know if you saw the Vanguard guy today in the keynote, he basically said, "We rip down, or tore down our big data infrastructure "moved it to the Cloud, spun up EMR." I mean there's a perfect example of, you got to bring your governance with you. You can't have to rebuild that whole stack. Are you in the Marketplace yet? >> Steve and Rob: Yes. >> You are, great, awesome. >> Yeah we launched a managed version of Immuta over the summer on AWS Marketplace. We'll be launching a second one shortly and it's really, the offering that we have out there is really geared toward, for lack of a better term, democratizing data governance. It's actually free up to the fifth user so any organization can deploy Immuta in under 30 minutes through Marketplace and start protecting their data. >> That's great, we had Dave McCann on yesterday, he runs the Marketplace, he was telling us just now, private offers for every marketplace, so ICV, so that's from. Last question I have is, how do you see this all playing out? You got GDPR, remember you talked about California regulations, there's a technology component, any predictions you guys want to share? What's your telescope say? >> All data will be regulated data eventually. So if you're not thinking about that now you need to. So, at least that's our theory, obviously, so we think it's critical that you're doing that from day one instead of day 365 and in your migration strategy. And if you're not thinking about that it's going to potentially bite you in the ass. >> Yeah you're right, I mean Web 2.0 was the wild, wild west, there was no privacy, there was no regulation, GDPR started to get people focused on that and it's now a whole new world. >> Gentlemen thank you, appreciate the time and best of luck. I know you said you had the big launch this summer but good things are ahead no doubt. >> For sure, thank you. >> Thank you. >> Dave Vellante: Thanks guys. >> Back with more coverage here on theCUBE. You're watching AWS re:Invent 2019. We are live and we're in Las Vegas. (upbeat tones)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Welcome everyone to this special CUBE Conversation Around the CUBE segment, Unpacking AI, number two, sponsored by Juniper Networks. We've got a great lineup here to go around the CUBE and unpack AI. We have Ken Jennings, all-time Jeopardy champion with us. Celebrity, great story there, we'll dig into that. John Hinson, director of AI at Evotek and Charna Parkey, who's the applied scientist at Textio. Thanks for joining us here for Around the CUBE Unpacking AI, appreciate it. First question I want to get to, Ken, you're notable for being beaten by a machine on Jeopardy. Everyone knows that story, but it really brings out the question of AI and the role AI is playing in society around obsolescence. We've been hearing gloom and doom around AI replacing people's jobs, and it's not really that way. What's your take on AI and replacing people's jobs? >> You know, I'm not an economist, so I can't speak to how easy it's going to be to retrain and re-skill tens of millions of people once these clerical and food prep and driving and whatever jobs go away, but I can definitely speak to the personal feeling of being in that situation, kind of watching the machine take your job on the assembly line and realizing that the thing you thought made you special no longer exists. If IBM throws enough money at it, your skill essentially is now obsolete. And it was kind of a disconcerting feeling. I think that what people need is to feel like they matter, and that went away for me very quickly when I realized that a black rectangle can now beat me at a game show. >> Okay John, what's your take on AI replacing jobs? What's your view on this? >> I think, look, we're all going to have to adapt. There's a lot of changes coming. There's changes coming socially, economically, politically. I think it's a disservice to us all to get to too indulgent around the idea that these things are going to change. We have to absorb these things, we have to be really smart about how we approach them. We have to be very open-minded about how these things are going to actually change us all. But ultimately, I think it's going to be positive at the end of the day. It's definitely going to be a little rough for a couple of years as we make all these adjustments, but I think what AI brings to the table is heads above kind of where we are today. >> Charna, your take around this, because the role of humans versus machines are pretty significant, they help each other. But is AI going to dominate over humans? >> Yeah, absolutely. I think there's a thing that we see over and over again in every bubble and collapse where, you know, in the automotive industry we certainly saw a bunch of jobs were lost, but a bunch of jobs were gained. And so we're just now actually getting into the phase where people are realizing that AI isn't just replacement, it has to be augmentation, right? We can't simply use images to replace recognition of people, we can't just use black box to give our FICO credit scores, it has to be inspectable. So there's a new field coming up now called explainable AI that actually is where we're moving towards and it's actually going to help society and create jobs. >> All right so let's stay on that next point for the next round, explainable AI. This points to a golden age. There's a debate around are we in a bubble or a golden age. A lot of people are negative right now on tech. You can see all the tech backlash. Amazon, the big tech companies like Apple and Facebook, there's a huge backlash around this so-called tech for society. Is this an indicator of a golden age coming? >> I think so, absolutely. We can take two examples of this. One would be where, you remember when Amazon built a hiring algorithm based upon their own resume data and they found that it was discriminating against women because they had only had men apply for it. Now with Textio we're building augmented writing across the audience and not from a single company and so companies like Johnson and Johnson are increasing the pipeline by more than nine percent which converts to 90,000 more women applying for their jobs. And so part of the difference there is one is explainable, one isn't, and one is using the right data set representing the audience that is consuming it and not a single company's hiring. So I think we're absolutely headed into more of a golden age, and I think these are some of the signs that people are starting to use it in the right way. >> John, what's your take? Obviously golden age doesn't look that to us right now. You see Facebook approving lies as ads, Twitter banning political ads. AI was supposed to solve all these problems. Is there light at the end of this dark tunnel we're on? >> Yeah, golden age for sure. I'm definitely a big believer in that. I think there's a new era amongst us on how we handle data in general. I think the most important thing we have here though is education around what this stuff is, how it works, how it's affecting our lives individually and at the corporate level. This is a new era of informing and augmenting literally everything we do. I see nothing but positives coming out of this. We have to be obviously very careful with our approaching all the biases that already exist today that are only going to be magnified with these types of algorithms at mass scale. But ultimately if we can get over that hurdle, which I believe collectively we all need to do together, I think we'd live in much better, less wasteful world just by approaching the data that's already at hand. >> Ken, what's your take on this? It's like a daily double question. Is it going to be a golden age? >> Laughs >> It's going to come sooner or later. We have to have catastrophe before, we have to have reality hit us in the face before we realize that tech is good, and shaping it? It's pretty ugly right now in some of the situations out there, especially in the political scene with the election in the US. You're seeing some negative things happening. What's your take on this? >> I'm much more skeptical than John and Charna. I feel like that kind of just blinkered, it's going to be great, is something you have to actually be in the tech industry and hearing all day to actually believe. I remember seeing kind of lay-person's exposure to Watson when Watson was on Jeopardy and hearing the questions reporters would ask and seeing the memes that would appear, and everyone's immediate reaction just to something as innocuous as a AI algorithm playing on a game show was to ask, is this Skynet from Terminator 2? Is this the computer from The Matrix? Is this HAL pushing us out of the airlock? Everybody immediately first goes to the tech is going to kill us. That's like everybody's first reaction, and it's weird. I don't know, you might say it's just because Hollywood has trained us to expect that plot development, but I almost think it's the other way around. Like that's a story we tell because we're deeply worried about our own meaning and obsolescence when we see how little these skills might be valued in 10, 20, 30 years. >> I can't tell you how much, by the way, Star Trek, Star Wars and Terminators probably affected the nomenclature of the technology. Everyone references Skynet. Oh my God, we're going to be taken over and killed by aliens and machines. This is a real fear. I thinks it's an initial reaction. You felt that Ken, so I've got to ask you, where do you think the crossover point is for people to internalize the benefits of say, AI for instance? Because people will say hey, look back at life before the iPhone, look at life before these tools were out there. Some will say society's gotten better, but yet there's this surveillance culture, things... And on and on. So what do you guys think the crossover point is for the reaction to change from oh my God, it's Skynet, gloom and doom to this actually could be good? >> It's incredibly tricky because as we've seen, the perception of AI both in and out of the industry changes as AI advances. As soon as machine learning can actually do a task, there's a tendency to say there's this no true Scotsman problem where we say well, that clearly can't be AI because I see how the trick worked. And yeah, humans lose at chess now. So when these small advances happen, the reaction is often oh, that's not really AI. And by the same token, it's not a game-changer when your email client can start to auto-complete your emails. That's a minor convenience to you. But you don't think oh, maybe Skynet is good. I really do think it's going to have to be, maybe the inflection point is when it starts to become so disruptive that actually public policy has to change. So we get serious about >> And public policy has started changing. >> whatever their reactions are. >> Charna, your thoughts. >> The public policy has started changing though. We just saw, I think it was in September, where California banned the use of AI in the body cameras, both real-time and after the fact. So I think that's part of the pivot point that we're actually seeing is that public policy is changing.` The state of Washington currently has a task force for AI who's making a set of recommendations for policy starting in December. But I think part of what we're missing is that we don't have enough digital natives in office to even attempt to, to your point Ken, predict what we're even going to be able to do with it, right? There is this fear because of misunderstanding, but we also don't have a respect of our political climate right now by a lot of our digital natives, and they need to be there to be making this policy. >> John, weigh in on this because you're director of AI, you're seeing positive, you have to deal with the uncertainty as well, the growth of machine learning. And just this week Google announced more TensorFlow for everybody. You're seeing Open Source. So there's a tech push, almost a democratization, going on with AI. So I think this crossover point might be sooner in front of us than people think. What's your thoughts? >> Yeah it's here right now. All these things can be essentially put into an environment. You can see these into products, or making business decisions or political decisions. These are all available right now. They're available today and its within 10 to 15 lines of code. It's all about the data sets, so you have to be really good stewards of the data that you're using to train your models. But I think the most important thing, back to the Skynet and all this science-fiction side, we have to collectively start telling the right stories. We need better stories than just this robots are going to take us over and destroy all of our jobs. I think more interesting stories really revolve around, what about public defenders who can have this informant augmentation algorithm that's going to help them get their job done? What about tailor-made medicine that's going to tell me exactly what the conditions are based off of a particular treatment plan instead of guessing? What about tailored education that's going to look at all of my strengths and weaknesses and present a plan for me? These are things that AI can do. Charna's exactly right, where if we don't get this into the right political atmosphere that's helping balance the capitalist side with the social side, we're going to be in trouble. So that's got to be embedded in every layer of enterprise as well as society in general. It's here, it's now, and it's real. >> Ken, before we move on to the ethics question, I want to get your thoughts on this because we have an Alexa at home. We had an Alexa at home; my wife made me get rid of it. We had an Apple device, what they're called... the Home pods, that's gone. I bought a Portal from Facebook because I always buy the earliest stuff, that's gone. We don't want listening devices in our house because in order to get that AI, you have to give up listening, and this has been an issue. What do you have to give to get? This has been a big question. What's your thoughts on all this? >> I was at an Amazon event where they were trumpeting how no technology had ever caught on faster than these personal digital assistants, and yet every time I'm in a use case, a household that's trying to use them, something goes terribly wrong. My friend had to rename his because the neighbor kids kept telling Alexa to do awful things. He renamed it computer, and now every time we use the word computer, the wall tells us something we don't want to know. >> (laughs) >> This is just anecdata, but maybe it speaks to something deeper, the fact that we don't necessarily like the feeling of being surveilled. IBM was always trying to push Watson as the star Trek computer that helpfully tells you exactly what you need to know in the right moment, but that's got downsides too. I feel like we're going to, if nothing else, we may start to value individual learning and knowledge less when we feel like a voice from the ceiling can deliver unto us the fact that we need. I think decision-making might suffer in that kind of a world. >> All right, this brings up ethics because I bring up the Amazon and the voice stuff because this is the new interface people want to have with machines. I didn't mention phones, Androids and Apple, they need to listen in order to make decisions. This brings up the ethics question around who sets the laws, what society should do about this, because we want the benefits of AI. John, you point out some of them. You got to give to get. Where are we on ethics? What's the opinion, what's the current view on this? John, we'll start with you on your ethics view on what needs to change now to move the ball faster. >> Data is gold. Data is gold at an exponential rate when you're talking about AI. There should be no situation where these companies get to collect data at no cost or no benefit to the end consumer. So ultimately we should have the option to opt out of any of these products and any of this type of surveillance wherever we can. Public safety is a little bit different situation, but on the commercial side, there is a lot of more expensive and even more difficult ways to train these models with a data set that isn't just basically grabbing everything our of your personal lives. I think that should be an option for consumers and that's one of those ethical check-marks. Again, ethics in general, the way that data's trained, the way that data's handled, the way models actually work, it has to be a primary reason for and approach of how you actually go about developing and delivering AI. That said, we cannot get over-indulgent in the fact that we can't do it because we're so fearful of the ethical outcomes. We have to find some middle ground and we have to find it quickly and collectively. >> Charna, what's your take on this? Ethics is super important to set the agenda for society to take advantage of all this. >> Yeah. I think we've got three ethical components here. We certainly have, as John mentioned, the data sets. However, it's also what behavior we're trying to change. So I believe the industry could benefit from a lot more behavioral science, so that we can understand whether or not the algorithms that we're building are changing behaviors that we actually want to change, right? And if we aren't, that's unethical. There is an entire field of ethics that needs to start getting put into our companies. We need an ethics board internally. A few companies are doing this already actually. I know a lot of the military companies do. I used to be in the defense industry, and so they've got a board of ethics before you can do things. The challenge is also though that as we're democratizing the algorithms themselves, people don't understand that you can't just get a set of data that represents the population. So this is true of image processing, where if we only used 100 images of a black woman, and we used 1,000 images of a white man because that was the distribution in our population, and then the algorithm could not detect the difference between skin tones for people of color, then we end up with situations where we end up in a police state where you put in an image of one black woman and it looks like ten of them and you can't distinguish between them. And yet, the confidence rate for the humans are actually higher, because they now have a machine backing their decision. And so they stop questioning, to your point, Ken, about what is the decision I'm making, they're like I'm so confident, this data told me so. And so there's a little bit of you need some expert in the loop and you also can't just have experts, because then you end up with Cambridge Analytica and all of the political things that happened there, not just in the US, but across 200 different elections and 30 different countries. And we are upset because it happened in the US, but this has been happening for years. So its just this ethical challenge of behavior change. It's not even AI and we do it all the time. Its why the cigarette industry is regulated (laughs). >> So Ken, what's your take on this? Obviously because society needs to have ethics. Who runs that? Companies? The law-makers? Someone's got to be responsible. >> I'm honestly a little pessimistic the general public will even demand this the way we're maybe hoping that they will. When I think about an example like Facebook, people just being able to, being willing to give away insane amounts of data through social media companies for the smallest of benefits: keeping in touch with people from high school they don't like. I mean, it really shows how little we value not being a product in this kind of situation. But I would like to see this kind of ethical decisions being made at the company-level. I feel like Google kind of surreptitiously moved away from it's little don't be evil mantra with the subtext that eh, maybe we'll be a little evil now. It just reminds me of Manhattan Project era thinking, where you could've gone to any of these nuclear scientists and said you're working on a real interesting puzzle here, it might advance the field, but like 200,000 civilians might die this summer. And I feel like they would've just looked at you and thought that's not really my bailiwick. I'm just trying to solve the fission problem. I would like to see these 10 companies actually having that kind of thinking internally. Not being so busy thinking if they can do something that they don't wonder if they should. >> That's a great point. This brings up the point of who is responsible. Almost as if who is less evil than the other person? Google, they don't do evil, but they're less evil than Amazon and Facebook and others. Who is responsible? The companies or the law-makers? Because if you look up some of the hearings in Washington, D.C., some of the law-makers we see up there, they don't know how the internet works, and it's pretty obvious that this is a problem. >> Yeah, well that's why Jack Dorsey of Twitter posted yesterday that he banned not just political ads, but also issue ads. This isn't something that they're making him do, but he understands that when you're using AI to target people, that it's not okay. At some point, while Mark is sitting on (laughs) this committee and giving his testimony, he's essentially asking to be regulated because he can't regulate himself. He's like well, everyone's doing it, so I'm going to do it too. That's not an okay excuse. We see this in the labor market though actually, where there's existing laws that prevent discrimination. It's actually the company's responsibility to make sure that the products that they purchase from any vendor isn't introducing discrimination into that process. So its not even the vendor that's held responsible, it's the company and their use of it. We saw in the NYPD actually that one of those image recognition systems came up and someone said well, he looked like, I forget the name of what the actor was, but some actor's name is what the perpetrator looked like and so they used an image of the actor to try and find the person who actually assaulted someone else. And that's, it's also the user problem that I'm super concerned about. >> So John, what's your take on this? Because these are companies are in business to make money, for profit, they're not the government. And who's the role, what should the government do? AI has to move forward. >> Yeah, we're all responsible. The companies are responsible. The companies that we work with, I have yet to interact with customers, or with our customers here, that have some insidious goal, that they're trying to outsmart their customers. They're not. Everyone's looking to do the best and deliver the most relevant products in the marketplace. The government, they absolutely... The political structure we have, it has to be really intelligent and it's got to get up-skilled in this space and it needs to do it quickly, both at the economy level, as well as for our defense. But the individuals, all of us as individuals, we are already subjected to this type of artificial intelligence in our everyday lives. Look at streaming, streaming media. Right now every single one of us goes out through a streaming source, and we're getting recommendations on what we should watch next. And we're already adapting to these things, I am. I'm like stop showing me all the stuff you know I want to watch, that's not interesting to me. I want to find something I don't know I want to watch, right? So we all have to get educated, we're all responsible for these things. And again, I see a much more positive side of this. I'm not trying to get into the fear-mongering side of all the things that could go wrong, I want to focus on the good stories, the positive stories. If I'm in a courtroom and I lose a court case because I couldn't afford the best attorney and I have the bias of a judge, I would certainly like artificial intelligence to make a determination that allows me to drive an appeal, as one example. Things like that are really creative in the world that we need to do. Tampering down this wild speculation we have on the markets. I mean, we are all victims of really bad data decisions right now, almost the worst data decisions. For me, I see this as a way to actually improve all those things. Fraud fees will be reduced. That helps everybody, right? Less speculation and these wild swings, these are all helpful things. >> Well Ken, John and Charna, thank- (audio feedback) >> Go ahead, finish. Get that word in. >> Sorry. I think that point you were making though John, is we are still a capitalist society, but we're no longer a shareholder capitalist society, we are a stakeholder capitalist society and the stakeholder is the society itself. It is us, it what we want to see. And so yes, I still want money. Obviously there are things that I want to buy, but I also care about well-being. I think it's that little shift that we're seeing that is actually you and I holding our own teams accountable for what they do. >> Yeah, culture first is a whole new shift going on in these companies that's a for-profit, mission-based. Ken, John, Charna, thanks for coming on Around the CUBE, Unpacking AI. Let's go around the CUBE Ken, John and Charna in that order, and just real quickly, unpacking AI, what's your final word? >> (laughs) I really... I'm interested in John's take that there's a democratization coming provided these tools will be available to everyone. I would certainly love to believe that. It seems like in the past, we've seen no, that access to these kind of powerful, paradigm-changing tools tend to be concentrated among a very small group of people and the benefits accrue to a very small group of people. But I hope that doesn't happen here. You know, I'm optimistic as well. I like the utopian side where we all have this amazing access to information and so many new problems can get solved with amazing amounts of data that we never could've touched before. Though you know, I think about that. I try to let that help me sleep at night, and not the fact that, you know... every public figure I see on TV is kind of out of touch about technology and only one candidate suggests the universal basic income, and it's kind of a crackpot idea. Those are the kind of things that keep me up at night. >> All right, John, final word. >> I think it's beautiful, AI's beautiful. We're on the cusp of a whole new world, it's nothing but positivity I see. We have to be careful. We're all nervous about it. None of us know how to approach these things, but as human beings, we've been here before. We're here all the time. And I believe that we can all collectively get a better lives for ourselves, for the environment, for everything that's out there. It's here, it's now, it's definitely real. I encourage everyone to hurry up on their own education. Every company, every layer of government to start really embracing these things and start paying attention. It's catching us all a little bit by surprise, but once you see it in production, you see it real, you'll be impressed. >> Okay, Charna, final word. >> I think one thing I want to leave people with is what we incentivize is what we end up optimizing for. This is the same for human behavior. You're training a new employee, you put incentives on the way that they sell, and that's, they game the system. AI's specifically find the optimum route, that is their job. So if we don't understand more complex cost functions, more complex representative ways of training, we're going to end up in a space, before we know it, that we can't get out of. And especially if we're using uninspectable AI. We really need to move towards augmentation. There are some companies that are implementing this now that you may not even know. Zillow, for example, is using AI to give you a cost for your home just by the photos and the words that you describe it, but they're also purchasing houses without a human in the loop in certain markets, based upon an inspection later by a human. And so there are these big bets that we're making within these massive corporations, but if you're going to do it as an individual, take a Coursera class on AI and take a Coursera class on ethics so that you can understand what the pitfalls are going to be, because that cost function is incredibly important. >> Okay, that's a wrap. Looks like we have a winner here. Charna, you got 18, John 16. Ken came in with 12, beaten again! (both laugh) Okay, Ken, seriously, great to have you guys on, a pleasure to meet everyone. Thanks for sharing on Around the CUBE Unpacking AI, panel number two. Thank you. >> Thanks a lot. >> Thank you. >> Thanks. I've been defeated by artificial intelligence again! (all laugh) (upbeat music)

>> from the Cosmopolitan Hotel in Las Vegas, Nevada. It's the Cube covering Cooper inspired 2019. Brought to You by Cooper. >> Welcome to the cue from Cooper inspired 19 at the Cosmopolitan in Las Vegas. I'm Lisa Martin, and I'm very pleased and honored to be joined by Dr Deepak Chopra, world renowned pioneer in integrative medicine and personal transformation. Doctor Chopper. What a pleasure to have you on a huge It's wonderful to be with. So here we are Ready Technology conference. I know you talk a lot of different types of guns, and if we look at technology these days, we can't get up without it, right? It's our alarm clock in the morning. We're listening to podcasts or radio dot Thomas. We're getting ready for work. It's an essential component of our allies, but also something that if you look on the other side, it's bombarding us constantly with opportunities to talk to this person or to buy this or that as an expert in the human brain and consciousness were some of the observations that you've seen where way can really tie together technology to help us be more mindful. >> My first world, you have to realize that technology is our creation in my opinion technologies, actually an aspect of human evolution it's now happening is part of a revolution. It's also an aspect of cultural evolution. So when you say we're constantly bombarded by it, that implies a certain element of victimization by our own creation. So we don't need to do that. You know, technologies neutral. You can hack with it. You can mess up in election with it, you can cause destruction with it. You can increase inflammation in the body with it by sending somebody an emoticon that is upsetting to them. Or you can use technology to heal yourself on. Ultimately heal the ecosystem and the world. So, personally, I am a big fan of technology. If you don't relate to technology, you will become irrelevant. That's a Darwinian principle. Either you adapt and use it or they're not. >> That's a really interesting way of putting it. You're right. If you're not using it and adopting it and being receptive to the positive changes that it can bring in our lives, you will be irrelevant. What are some of your recommendations for people everyday people to be able to use it for just getting more center rather than protect my email. I have attacks. I have to respond to my >> so my push the activity Every days I have technology time, morning and afternoon. Have relationship game. Have meditation time, healthy eating time have playtime, recreation time have slipped out. So whenever you're doing something, you do it with full awareness. Whether it's technology speaking to another person, the most important activity in your life is what you're doing. Right now. The most important person in your life is the one in front of you Right now. Most of one thing to do with technologies to be fully engaged only when you're doing not, otherwise, schedule it. >> I love that. I love that you have all of these great times. A scheduled part of mutes wondered how much of this is psychological about actually controlling yourself? That's sort of common sense, but it's also in this day and age one of the hardest things to do here we are at a conference about business Spend management, where Cooper is talking to their businesses and every industry about you need to have control over your budget over your spend. It's sort of the same thing with technology. How do we actually use it to establish those schedules established that control that allows to take advantage of it also allows us to sit back, relax and enjoy the Now, >> you know, I don't like the word control, obviously. Okay, My word for that is be aware. So be aware of yourself and be aware of the fact that everything that's happening to you in the world is reflection of yourself. So if you find the world insane that question your sanity if you find the world melodramatic, hysterical question, your aspect of melodrama and his Syria, if you find the world centre, it's because your center and so the Boston born thing is self awareness, period >> like that and you're right. That's a >> much more you for Mr Ward, then control awareness. It's It's a more peaceful, I think, More action taking word. So I listened. T you started a podcast series this year. Infinite potential. So I know that you're not only using technology to continue reaching the folks who've been following you for many years, but now a new audience getting to tell stories in a different way. And I heard a two part podcast years where you were talking about a I and so one of the things that I wanted to talk to you about is this deep. So how are you leveraging a I to share your daily reflections, reach a bigger audience and help us become more aware? >> So my personal interest all my life as you mentioned, is well being personal transformation. I'm using deep learning, artificial intelligence, augmented immersive experiences, virtual reality, biological feedback, neuro plasticity, epi, genetics, all as a means for well being and personal transformation. So the future well being is very precise. It's very personalized because no two people react to the same similares, whether it's a diet or a compliment or in a front in the same way artificial intelligence can. If you want, help me know everything about you. Everything, how your mind works, how your emotions work, how your body works and the relationship with that. So one of the things I'm examining right now is 2,000,000 jeans in our body which are not human, which microbial is called the microbe microbiome. It's actually as significant as human genes in determining your state of well being by analyzing the microbiome through artificial intelligence and deep learning. You can killer well being interventions very personally and very predictably and, of course, requiring your participation. You become your own healer of co healer in the sense artificial intelligence for deep leading off gene expression. Not just jeans. Because genes are not now owns their verbs. What are they doing? What are they up to right now? The genes that are responsible for healing active are the genes that are responsible for inflammation or disease Inactive. What most of your audience may not know is that only 5% of genetic mutations that give rise to disease fully penetrate, which means only 5%. Which means the guarantee. The disease. If you have ah Braca gene for breast cancer, you're going to get breast cancer for that. Also, new technologies like Christmas you'll be able to read the barcode of a gene, cut the hunt footed or deleting harmful Julin Jean insert the healthy, and so that will solve that problem. And it's happening very soon. It's in the works, but 95% of illness, even with the genetic mutations that predispose you to a less, are not predictable dependent on your lifestyle. Now it was in the past. You couldn't measure that. Today you can. You can measure sleep. You can measure dream, sleep deep sleep. You can measure exercise. You can measure heart rate variability. You can measure gene expression and you can digitize the whole thing. So with that, we have an amazing new frontier in medicine. The three dimensional model of pharmaceuticals has very limited application, only an acute illness. The future off treatment even will be through technology. So in five years you go to a doctor's office. They might give you a V R session instead of writing a prescription. >> Well, in a lot of advanced technologies are being utilized now in medicine, seeing a doctor virtually through computer, exactly telemedicine being able to treat more people faster. But it's like were in >> the first minute of on there if I >> were in the in the puberty. Yeah, you know, puberty is a time of challenge, and >> true and and >> so were the adolescence of our use of technology is getting richer. >> So when we look at all of the applications for the emerging technologies that you mentioned it so much good that can happen, we can become so much more aware of our own and take don't take control. I know you don't like that word, but take ownership, Influence, Influence Yes, >> if we look at some of the negative consequences of artificial intelligence machine learning. I was fascinated by your podcast with Christopher Whitely and how incredibly potent Cambridge Analytica waas in changing the course of American history. >> And it could ruin democracy. Yes, So we need to have surveillance. We need to have, you know, chords for keeping it secure. Yes. So even these problems, by the way, can be solved by technologies >> they can. It's sort of a catch >> 22 isn't it? >> Yes, but the same time here we are, freely as just consumers. And one of the things that Cooper is talking about is making a purchasing decision, making buying management in business. As easy as it is for us consumers, you know you need something, you go on amazon dot com and there is click to buy. It shows up so quickly you've forgotten what you ordered. It's like your birthday. So there are so many advantages. At the same time, it's creating a lot of challenges with >> this conversation is going to help solve those challenges because the more we have this conversation in social media, in education facilities, even an entertainment, we're writing a new story together. >> And that story is that narrative is so powerful. Yes, absolutely. You're right. It's everything but going back to your word awareness. That's what So money, whatever the causes, really needs to have us that consistent. It's not just saying it a few times here. They're on different media, right? It's not consistent, >> consistent messaging. And in my mind that messaging is one thing. It's been my mission statement for the last 35 years. Way have to accelerate collective consciousness in the direction of a more peaceful, just sustainable, healthier and joyful work. We have to eliminate war. We have to eliminate equal destruction. We have to eliminate to climate change way have the technology to do it. But now we need to harness the collective intelligence, the collective creativity and the collective impulse for love and compassion to technology, and we'll do it. >> I like that. You sound very definitive. We will do it first, though some of those naysayers who don't believe climate change Israel, for example, How do you advise whether it's a government organization for people to start looking at? Use the technology? Look at the data, start being receptive to the fact that changes happening. But we could harness the power of it for so many good application. >> It was in this year's. It's not without arguing with them on. Data helps, but scientific data never changed a broader revolution. You need data. You need science, which you need collective emotional connection. If you don't have that emotional and spiritual connection, if you don't see that the air is your breath. If you don't see that the rivers and waters in the ocean are your circulation. If you don't see that the earth is recycling is your body. If you don't see that what we call the environment is their extended body. You have a personal body and the university body, and if you're not emotionally tied to that, then scientific did does >> such an interesting concept. We just think, Well, the data's there, it shows this. Therefore, it is what you're saying. We have to have an emotional connection. >> Yes, data by itself, science were itself faxed by itself. Don't change the world. But when facts are tied to an emotional story, everything changes. >> So, wrapping things up here, I know that you are working to create a diversion of Dr Deepak Chopra that will live forever that will be able to continue to inspire. Many generations >> have been working on this. It's actually a stealth project, so I can't give details. But I've been working on this for more than a year now, and where we are is I will soon have a version of myself, my mind twin that will know everything that I've ever said. But we'll also through deep learning, continue to learn and we lived for generations are from gone or perhaps eternally and we'll communicate with the world even when I'm physically nor president and because it will be learning as we go along and incorporating everything into my take on what is reality. What is fundamental reality, what is consciousness? It will be much smarter than I am. >> So you think that a I and consciousness are really going to be able Thio merge together to continue to evolve rather than you think about a way I take stated from the past and the present to try to predict the future. But you see them as living some bio symbiotically, eh? I >> do. But we have to be careful here will never have subjective consciousness. Okay? Never. It may replicate insight and intuition and creativity and even vision, but it won't be able to fall in love. >> That's good. I was a little worried about that on >> it will not be able to address experientially what comes from, um, meditation and other reflective enquiries that transcend human thought. So, you know, science is a system of thought, just like mythology, religion, philosophy, theology, our systems of thought. No system of thought can actually access reality till you go to the source of thought, which is consciousness >> source thought. Dr. Deepak Chopra. What a pleasure to have you on the Cube. Thank you so much for joining me this morning. I know you've got to get off your keynote, but it was very much a pleasure. >> Thank you. My pleasure. >> Excellent for Dr Deepak Chopra. I'm Lisa Martin. You're watching the Cube from Cooper inspired 19. Thanks for watching

>> live from Boston, Massachusetts. It's the you covering your red have some twenty nineteen rots. You buy bread hat. >> Good to have you back here on the Cube as we continue our coverage. Live at the Red Had Summit twenty nineteen, Day three of our coverage with you since Tuesday. And now it's just fresh off the keynote stage, joining stew, Minutemen and myself. Chris. Right? VP and chief technology officer at Red Hat. Good job there, Chris. Thanks for being with us this morning. Yeah. >> Thank you. Glad to be here. >> Great. Right? Among your central things, you talked about this, this new cycle of innovation and those components and how they're integrating to create all these great opportunities. So if you would just share for those with those at home who didn't have an opportunity to see the keynote this morning, it's what you were talking about. I don't think they play together. And where that lies with red hat. Yeah, you bet. >> So, I think an important first kind of concept is a lot of what we're doing. Is lane a foundation or a platform? Mean red hats focuses in the platform space. So we think of it as building this platform upon which you build an innovate. And so what we're seeing is a critical part of the future is data. So we're calling it a Kino data centric. It's the data centric economy. Along with that is machine learning. So all the intelligence that comes, what do you dividing? The insights you're grabbing from that data. It introduces some interesting challenges data and privacy and what we do with that data, I mean, we're all personally aware of this. You see the Cambridge Analytica stuff, and you know, we all have concerns about our own data when you combine all of us together techniques for how we can create insights from data without compromising privacy. We're really pushing the envelope into full distributed systems, EJ deployments, data coming from everywhere and the insights that go along with that. So it's really exciting time built on a consistent platform like lycopene shift. >> So, Chris, I always loved getting to dig in with you because that big trend of distributed systems is something that you know we've been working on for quite a long time. But, you know, we fully agree. You said data at the center of everything and that roll of even more distributed system. You know, the multi cloud world. You know, customers have their stuff everywhere and getting their arms around that, managing it, being about leverage and take advantage. That data is super challenging. So you know where where, you know, help us understand some of the areas that red hat in the communities are looking to solve those problems, you know, where are we and what's going well and what's still left to work on. >> Well, there's a couple of different aspect. So number one we're building these big, complex systems. Distributed systems are challenging distribute systems, engineers, air really solving harder problems. And we have to make that accessible to everybody operations teams. And it's one of the things that I think the cloud taught us when you sort of outsource your operations is somebody else. You get this encapsulated operational excellence. We need to bring that to wherever your work clothes are running. And so we talked a lot about a I ops, how you harness the value of data that's coming out of this complex infrastructure, feed it through models and gain insights, and then predict and really Ultimately, we're looking at autonomic computing how we can create autonomous clouds, things that really are operating themselves as much as possible with minimal human intervention. So we get massive scale. I think that's one of the key pieces. The other one really talking about a different audience. The developers. So developers air trying to incorporate similar types of intelligence into their applications were making recommendations. You're tryingto personalize applications for end users. They need easy access to that data. They need easy access to train models. So how do we do that? How do we make that challenging data scientist centric workflow accessible to developers? >> Yeah, just some of the challenges out there. I think about, you know, ten, fifteen years ago, you talk to people, it was like, Well, I had my central source of truth and it was a database. And you talk to most companies now and it's like, Well, I've got a least a dozen different database and you know, my all my different flavors of them and whether in the cloud or whether I have them in my environment, you know, things like a ops trying to help people get involved with them. You talked a little bit in your keynote about some of the partners that you're working on. So how do you, you know, bring these together and simplify them when they're getting, you know, even more and more fragmented? >> Well, it's part of the >> challenge of innovation. I mean, I think there's a there's a natural cycle. Creativity spawns new ideas. New ideas are encapsulated in projects, so there's a wave of expansion in any kind of new technology time frame. And then there's ultimately, you see some contraction as we get really clear winners and the best ideas and in the container orchestration space communities is a great example of that. We had a lot of proliferation of different ways of doing it. Today we're consolidating as an industry around Cooper Netease. So what we're doing is building a platform, building a rich ecosystem around that platform and bringing our partners in who have specific solutions. They look at whether it's the top side of the house, talking to the operations teams or whether it's giving developers easy access to data and training models through some partners that we had today, like perceptive labs and each to a A I this partnership. Bringing it to a common platform, I think, is a critical part of helping the industry move forward and ultimately will see where these best of breed tools come into play. >> Here, uh, you know, maybe help a little bit with with in terms of practical application, you got, you know, open source where you've got this community development going on and then people customized based on their individual needs all well, great, right? How does the inverse happen? Where somebody who does some custom ization and comes up with a revelation of some kind and that applies back to the general community. And we can think of a time where maybe something I'm thinking like Boston children, their imaging, that hospital we saw actually related to another industry somehow and gave them an ah ha moment that maybe they weren't expecting an open source. Roy was the driver that >> Yeah, I think what we showed today were some examples of what If you distill it down to the core, there's some common patterns. There's data, they're streaming data. There's the data processing, and there's a connection of that processed data or train model to an application. So we've been building an open source project called Open Data Hub, where we can bring people together to collaborate on what are the tools that we need to be in this stack of this kind of framework or stack And and then, as we do, that we're talking to banks. They're looking at any money laundering and fraud detection. We're talking to these hospitals that were looking at completely different use cases like HC Healthcare, which is taking data to reduce the amount of time nurses need to spend, gathering information from patients and clearly identify Septus sepsis concerns totally different applications, similar framework. And so getting that industry level collaboration, I think is the key, and that having common platforms and common tools and a place to rally around these bigger problems is exactly how we do that through open source. >> So Lynn exits and an interesting place in the stack is you talked about the one commonality and everything like that. But we're actually at a time where the proliferation of what's happen to get the hardware level is something that you know of an infrastructure and harbor guy by background, and it was like, Oh, I thought We're going to homogenize everything, standardize everything, and it's like, Oh, you're showing off Colin video stuff. And when we're doing all these pieces there, there's all these. You know, new things, Every been things you know you work from the mainframe through the latest armed processors. Give us a little insight as to how your team's geeking out, making sure that they provide that commonality yet can take advantage of some of the cool, awesome stuff that's out there that's enabling that next wave of innovation. >> Yeah, so I share that infrastructure geek nous with you. So I'm so stoked the word that we're in this cycle of harbor innovation, I'll say something that maybe you sounds controversial if we go back in time just five years or a little, a little more. The focus was around cloud computing and bringing massive number of APS to the cloud, and the cloud had kind of a T shirt size, small, medium, large view of the world of computer. It created this notion that Khun computers homogenous. It's a lie. If you go today to a cloud provider and count the number of different machine types they have or instance types it's It's not just three, it's a big number. And those air all specialized. It's for Io throughput. It's for storage acceleration. It's big memory, you know. It's all these different use cases that are required for the full set of applications. Maybe you get the eighty percent in a common core, but there's a whole bunch of specific use cases that require performance optimization that are unique. And what we're seeing, I think, is Moore's law. The laws of physics are kind of colliding a little bit, and the way to get increased acceleration is through specialized hardware. So we see things like TP use from Google. We see until doing deal boost. We've got GPS and even F p G A s and the operating system is there TIO give a consistent application run time while enabling all those hardware components and bringing it all together so the applications can leverage the performance acceleration without having to be tied directly to it. >> Yeah, you actually think you wrote about that right now, one of your a block post that came about how hardware plays this hugely important role. You also talked about innovation and change happening incrementally and And that's not how we kind of think about like big Banks, right? Yeah. Wow, this is But you pointed out in the open source, it really is step by step by step. Which way? Think about disruption is being very dramatic. And there's nothing sexy about step by step. Yeah, that's how we get to Yeah, disruption. I kind of >> hate this innovation, disruption and their buzz words. On the one hand, that's what captures attention. It's not necessarily clear what they mean. I like the idea that, you know, in open source, we do every day, incremental improvements. And it's the culmination of all these improvements over time that unlock new opportunities. And people ask me all the time, where is the future? What do we do and what's going on? You know, we're kind of doing the same thing we've been doing for a long time. You think about micro services as a way to encapsulate functionality, share and reuse with other developers. Well, object oriented programming decades ago was really tryingto tryingto established that same capability for developers. So, you know, the technologies change we're building on our history were always incrementally improving. You bring it all together. And yes, occasionally you can apply that in a business case that totally disrupts an industry and changes the game. But I really wanted encourage people to think about what are the incremental changes you can make to create something fundamentally new. >> All right, I need to poke it that a little bit, Chris, because there's one thing you know, I looked back in my career and look back a decade or two decades. We used to talk about things like intelligence and automation. Those have been around my entire career. Yeah, you look it today, though, you talk about intelligence and talk about automation, it's not what we were doing, you know, just the amount of degrees, what we're having there. It is like if we'd looked at it before, it was like, Oh, my gosh, science fiction's here so, you know, way sometimes lose when we're doing step by step, that something's there making step function, improvements. And now the massive compact, massive changes. So love your opinions there. >> Yeah, well, I think it's a combination, so I talk about the perpetual pursuit of excellence. So you pick up, pick a field, you know, we're talking about management. We got data and how you apply that data. We've been working towards autonomic computing for decades. Concepts and research are old, the details and the technologies and the tools that we have today are quite different. But I'm not. You know, I'm not sure that that's always a major step function. I think part of that is this incremental change. And you look at the number for the amount of kind of processing power and in the GPU today No, this is a problem that that industry has been working on for quite a long time. At some point, we realize, Hey, the vector processing capabilities in the GPU really, really suit the machine learning matrix multiplication world real world news case. So that was a fundamental shift which unlocked a whole bunch of opportunity in terms of how we harness data and turn it into knowledge. >> Yes. So are there any areas that you look at? Now that we've been working at that, you feel we're kind of getting to those tipping points or the thie waves of technology or coming together to really enable Cem Cem massive change? >> I do think our ability to move data around, like generate data. For one thing, move data around efficiently, have access to it from a processing capability. And turning that into ah, >> model >> has so fundamentally changed in the past couple of decades that we are tapping into the next generation of what's possible and things like having this. This holy grail of a self healing, self optimizing, self driving cluster is not as science fiction as it felt twenty years ago. It's >> kind of exciting. You talk about you've been there in the past, the president, but there is very much a place in the future, right? And how would that future looks like just from from again? That aye aye perspective. It's a little scary, sometimes through to some people. So how are you going about, I guess, working with your partners to bring them along and accept certain notions that maybe five six years ago I've been a little tough to swallow or Teo feel comfortable with? >> Yeah, well, there's a couple of different dimensions there. One is, uh, finding tasks that air computers are great at that augment tasks that humans were great at and the example we had today. I love the example, which was, Let's have computers, crunch numbers and nurses do what they do best, which is provide care and empathy for the patients. So it's not taking the nurse's job away. In fact, is taking the part that is drudgery ITT's computation >> and you forget what was the >> call it machine enhanced human intelligence right on a couple of different ways of looking at that, with the idea that we're not necessarily trying to eliminate humans out of the loop. We're trying to get humans to do what they do best and take away the drudgery that computers air awesome at repetitive tasks. Big number crunching. I think that's one piece. The other pieces really, from that developer point of view, how do you make it easily accessible? And then the one step that needs to come after that is understanding the black box. What happens inside the machine learning model? How is it creating the insights that it's creating and there's definitely work to be done there? There's work that's already underway. Tto help understand? Uh, the that's really what's behind the inside so that we don't just trust, which can create some problems when we're introducing data that itself might already be biased. Then we assumed because we gave data to a computer which is seemingly unbiased, it's going to give us an unbiased result, right? Garbage in garbage out. >> So we got really thoughtful >> about what the models are and what the data is that we're feeding >> It makes perfect sense it. Thanks for the time. Good job on the keynote stage again this morning. I know you've got a busy afternoon scheduled as well, so yeah, I will let you. We'Ll cut you loose. But thank you again. Always good to see you. >> Yeah. I always enjoy being here >> right at that's right. Joining us from red hat back with Wharton Red Hat Summit forty nineteen. You're watching live here on the Cube?

>> From Boston, Massachusetts, it's the cube. covering LiveWorx 18, brought to you by PTC. (upbeat music) >> Welcome back to the Boston Seaport everybody. My name is David Vellante, I'm here with my co-host Stu Miniman and you're watching the cube, the leader in live tech coverage. We're at LiveWorx PTC's big IOT conference. Jaron Lanier is here, he's the father of virtual reality and the author of Dawn of the New Everything. Papa, welcome. >> Hey there. >> What's going on? >> Hey, how's it going? >> It's going great. How's the show going for you? It's cool, it's cool. It's, it's fine. I'm actually here talking about this other book a little bit too, but, yeah, I've been having a lot of fun. It's fun to see how hollow lens applied to a engines and factories. It's been really cool to see people seeing the demos. Mixed reality. >> Well, your progeny is being invoked a lot at the show. Everybody's sort of talking about VR and applying it and it's got to feel pretty good. >> Yeah, yeah. It seems like a VR IoT blockchain are the sort of the three things. >> Wrap it all with digital transformation. >> Yeah, digital transformation, right. So what we need is a blockchain VR IoT solution to transform something somewhere. Yeah. >> So tell us about this new book, what it's called? >> Yeah. This is called the deleting all your social media accounts right now. And I, I realize most people aren't going to do it, but what I'm trying to do is raise awareness of how the a psychological manipulation algorithms behind the system we're having an effect on society and I think I love the industry but I think we can do better and so I'm kind of agitating a bit here. >> Well Jaron, I was reading up a little bit getting ready for the interview here and people often will attack the big companies, but you point at the user as, you know, we need to kind of take back and we have some onus ourselves as to what we use, how we use it and therefore can have impact on, on that. >> Well, you know, what I've been finding is that within the companies and Silicon Valley, a lot of the top engineering talent really, really wants to pursue ethical solutions to the problem, but feels like our underlying business plan, the advertising business plan keeps on pulling us back because we keep on telling advertisers we have yet new ways to kind of do something to tweak the behaviors of users and it kind of gradually pulls us into this darker and darker territory. The thing is, there's always this assumption, oh, it's what users want. They would never pay for something the way they pay for Netflix, they would never pay for social media that way or whatever it is. The thing is, we've never asked users, nobody's ever gone and really checked this out. So I'm going to, I'm kind of putting out there as a proposition and I think in the event that users turn out to really want more ethical social media and other services by paying for them, you know, I think it's going to create this enormous sigh of relief in the tech world. I think it's what we all really want. >> Well, I mean ad-based business models that there's a clear incentive to keep taking our data and doing whatever you want with it, but, but perhaps there's a better way. I mean, what if you're, you're sort of proposing, okay, maybe users would be willing to pay for various services, which is probably true, but what if you were able to give users back control of their data and let them monetize their data. What are your thoughts on that? >> Yeah, you know, I like a lot of different solutions, like personally, if it were just up to me, if I ran the world, which I don't, but if I ran the world, I can make every single person of the world into a micro-entrepreneur where they can package, sell and price their data the way they want. They can, they can form into associations with others to do it. And they can also purchase data from others as they want. And I think what we'd see is this flowering of this giant global marketplace that would organize itself and would actually create wonders. I really believe that however, I don't run the world and I don't think we're going to see that kind of perfect solution. I think we're going to see something that's a bit rougher. I think we might see something approximating that are getting like a few steps towards that, but I think we are going to move away from this thing where like right now if two people want to do anything on online together, the only way that's possible is if there's somebody else who's around to pay them, manipulate them sneakily and that's stupid. I mean we can be better than that and I'm sure we will. >> Yeah, I'm sure we will too. I mean we think, we think blockchain and smart contracts are a part of that solution and obviously a platform that allows people to do exactly what you just described. >> And, and you know, it's funny, a lot of things that sounded radical a few years ago are really not sounding too radical. Like you mentioned smart contracts. I remember like 10 years ago for sure, but even five years ago when you talked about this, people are saying, oh no, no, no, no, no, this, the world is too conservative. Nobody's ever going to want to do this. And the truth is people are realizing that if it makes sense, you know, it makes sense. And, and, and, and so I think, I think we're really seeing like the possibilities opening up. We're seeing a lot of minds opening, so it's kind of an exciting time. >> Well, something else that I'd love to get your thoughts on and we think a part of that equation is also reputation that if you, if you develop some kind of reputation system that is based on the value that you contribute to the community, that affects your, your reputation and you can charge more if you have a higher reputation or you get dinged if you're promoting fake news. That that reputation is a linchpin to the successful community like that. >> Well, right now the problem is because, in the free model, there's this incredible incentive to just sort of get people to do things instead of normal capitalist. And when you say buy my thing, it's like you don't have to buy anything, but I'm going to try to trick you into doing something, whatever it is. And, and, and if you ever direct commercial relationship, then the person who's paying the money starts to be a little more demanding. And the reason I'm bringing that up is that right now there's this huge incentive to create false reputation. Like in reviews, a lot of, a lot of the reviews are fake, followers a lot of them are fake instance. And so there's like this giant world of fake stuff. So the thing is right now we don't have reputation, we have fake reputation and the way to get real reputation instead of think reputation is not to hire an army of enforcing us to go around because the company is already doing that is to change the financial incentives so you're not incentivizing criminals, you know I mean, that's incentives come first and then you can do the mop up after that, but you have to get the incentives aligned with what you want. >> You're here, and I love the title of the book. We interviewed James Scott and if you know James Scott, he's one of the principals at ICIT down PTC we interviewed him last fall and we asked him, he's a security expert and we asked them what's the number one risk to our country? And he said, the weaponization of social media. Now this is, this is before fake news came out and he said 2020 is going to be a, you know, what show and so, okay. >> Yeah, you know, and I want to say there's a danger that people think this is a partisan thing. Like, you know, if you, it's not about that. It's like even if you happen to support whoever has been on, on the good side of social media manipulation, you should still oppose the manipulation. You know, like I was, I was just in the UK yesterday and they had the Brexit foot where there was manipulation by Russians and others. And you know, the point I've made over there is that it's not about whether you support Brexit or not. That's your business, I don't even have an opinion. It's not, I'm an American. That's something that's for somebody else. But the thing is, if you look at the way Brexit happened, it tore society apart. It was nasty, it was ugly, and there have been tough elections before, but now they're all like that. And there was a similar question when the, the Czechoslovakia broke apart and they didn't have all the nastiness and it's because it was before social media that was called the velvet divorce. So the thing is, it's not so much about what's being supported, whatever you think about Donald Trump or anything else, it's the nastiness. It's the way that people's worst instincts are being used to manipulate them, that's the problem. >> Yeah, manipulation denial is definitely a problem no matter what side of the aisle you're on, but I think you're right that the economic incentive if the economic incentive is there, it will change behavior. And frankly, without it, I'm not sure it will. >> Well, you know, in the past we've tried to change the way things in the world by running around in outlying things. For instance, we had prohibition, we outlawed, we outlawed alcohol, and what we did is we created this underground criminal economy and we're doing something similar now. What we're trying to do is we're saying we have incentives for everything to be fake, everything to be phony for everything to be about manipulation and we're creating this giant underground of people trying to manipulate search results or trying to manipulate social media feeds and these people are getting more and more sophisticated. And if we keep on doing this, we're going to have criminals running the world. >> Wonder if I could bring the conversation back to the virtual reality. >> Absolutely. >> I'm sorry about that. >> So, but you know, you have some concerns about whether virtual reality will be something you for good or if it could send us off the deep end. >> Oh yeah, well. Look, there's a lot to say about virtual reality. It's a whole world after all. So you can, there is a danger that if the same kinds of games are being played on smartphones these days were transferred into a virtual reality or mixed reality modalities. Like, you could really have a poisonous level of mind control and I, I do worry about that I've worried about that for years. What I'm hoping is that the smartphone era is going to force us to fix our ways and get the whole system working well enough so that by the time technologies like virtual reality are more common, we'll have a functional way to do things. And it won't, it won't all be turned into garbage, you know because I do worry about it. >> I heard, I heard a positive segment on NPR saying that one of the problems is we all stare at our phones and maybe when I have VR I'll actually be talking to actual people so we'll actually help connections and I'm curious to hear your thoughts on that. >> Well, you know, most of the mixed reality demos you see these days are person looking at the physical world and then there's extra stuff added to the physical world. For instance, in this event, just off camera over there, there's some people looking at automobile engines and seeing them augmented and, and that's great. But, there's this other thing you can do which is augmenting people and sometimes it can be fun. You can put horns or wings or long noses or something on people. Of course, you still see them with the headsets all that's great. But you can also do other stuff. You can, you can have people display extra information that they have in their mind. You can have more sense of what each other are thinking and feeling. And I actually think as a tool of expression between people in real life, it's going to become extremely creative and interesting. >> Well, I mean, we're seeing a lot of applications here. What are some of your favorites? >> Oh Gosh. Of the ones right here? >> Yes. >> Well, you know, the ones right here are the ones I described and I really like them, there's a really cool one of some people getting augmentation to help them maintain and repair factory equipment. And it's, it's clear, it's effective, it's sensible. And that's what you want, right? If you ask me personally what really, a lot of the stuff my students have done, really charms me like up, there was just one project, a student intern made where you can throw virtual like goop like paint and stuff around in the walls and it sticks and starts running down and this is running on the real world and you can spray paint the real world so you can be a bit of a juvenile delinquent basically without actually damaging anything. And it was great, it was really fun and you know, stuff like that. There was this other thing and other student did where you can fill a whole room with these representations of mathematical objects called tensors and I'm sorry to geek out, but you had this kid where all these people could work together, manipulating tensors and the social environment. And it was like math coming alive in this way I hadn't experienced before. That really was kind of thrilling. And I also love using virtual reality to make music that's another one of my favorite things, >> Talk more about that. >> Well, this is something I've been doing forever since the '80s, since the '80s. I've been, I've been at this for awhile, but you can make an imaginary instruments and play them with your hands and you can do all kinds of crazy things. I've done a lot of stuff with like, oh I made this thing that was halfway between the saxophone and an octopus once and I'll just >> Okay. >> all this crazy. I love that stuff I still love it. (mumbling) It hasn't gotten old for me. I still love it as much as I used to. >> So I love, you mentioned before we came on camera that you worked on minority report and you made a comment that there were things in that that just won't work and I wonder if you could explain a little bit more, you know, because I have to imagine there's a lot of things that you talked about in the eighties that, you know, we didn't think what happened that probably are happening. Well, I mean minority report was only one of a lot of examples of people who were thinking about technology in past decades. Trying to send warnings to the future saying, you know, like if you try to make a society where their algorithms predicting what'll happen, you'll have a dystopia, you know, and that's essentially what that film is about. It uses sort of biocomputer. They're the sort of bioengineered brains in these weird creatures instead of silicon computers doing the predicting. But then, so there are a lot of different things we could talk about minority report, but in the old days one of the famous VR devices which these gloves that you'd use to manipulate virtual objects. And so, I put a glove in a scene mockup idea which ended up and I didn't design the final production glove that was done by somebody in Montreal, but the idea of putting a glove a on the heroes hand there was that glove interfaces give you arm fatigue. So the truth is if you look at those scenes there physically impossible and what we were hoping to do is to convey that this is a world that has all this power, but it's actually not. It's not designed for people. It actually wouldn't work in. Of course it kind of backfired because what happened is the production designers made these very gorgeous things and so now every but every year somebody else tries to make the minority report interface and then you discover oh my God, this doesn't work, you know, but the whole point was to indicate a dystopian world with UI and that didn't quite work and there are many other examples I could give you from the movie that have that quality. >> So you just finished the book. When did this, this, this go to print the. >> Yeah, so this book is just barely out. It's fresh from the printer. In fact, I have this one because I noticed a printing flaw. I'm going to call the publisher and say, Oh, you got to talk to the printer about this, but this is brand new. What happened was last year I wrote a kind of a big book of advert triality that's for real aficionados and it's called Dawn of the new everything and then when I would go and talk to the media about it they'd say, well yeah, but what about social media? And then all this stuff, and this was before it Cambridge Analytica, but people were still interested. So I thought, okay, I'll do a little quick book that addresses what I think about all that stuff. And so I wrote this thing last year and then Cambridge Analytica happened and all of a sudden it's, it seems a little bit more, you know, well timed >> than I could have imagined >> Relevant. So, what other cool stuff are you working on? >> I have to tell you something >> Go ahead. >> This is a real cat. This is a black cat who is rescued from a parking lot in Oakland, California and belongs to my daughter. And he's a very sweet cat named Potato. >> Awesome. You, you're based in Northern California? >> Yeah, yeah, yeah. >> Awesome And he was, he was, he was an extra on the set of, of the Black Panther movie. He was a stand-in for like a little mini black panthers. >> What other cool stuff are you working on? What's next for you? >> Oh my God, there's so much going on. I hardly even know where to begin. There's. Well, one of the things I'm really interested in is there's a certain type of algorithm that's really transforming the world, which is usually called machine learning. And I'm really interested in making these things more transparent and open so it's less like a black box. >> Interesting. Because this has been something that's been bugging me you know, most kinds of programming. It might be difficult programming, but at least the general concept of how it works is obvious to anyone who's program and more and more we send our kids to coding camps and there's just a general societal, societal awareness of what conventional programming is like. But machine learning has still been this black box and I view that as a danger. Like you can't have society run by something that most people feel. It's like this black box because it'll, it'll create a sense of distrust and, and, I think could be, you know, potentially quite a problem. So what I want to try to do is open the black box and make it clear to people. So that's one thing I'm really interested in right now and I'm, oh, well, there's a bunch of other stuff. I, I hardly even know where to begin. >> The black box problem is in, in machine intelligence is a big one. I mean, I, I always use the example I can explain, I can describe to you how I know that's a dog, but I really can't tell you how I really know it's a dog. I know I look at a dog that's a dog, but. Well, but, I can't really in detail tell you how I did that but it isn't AI kind of the same way. A lot of AI. >> Well, not really. There's, it's a funny thing right now in, in, in the tech world, there are certain individuals who happen to be really good at getting machine language to work and they get very, very well paid. They're sort of like star athletes. But the thing is even so there's a degree of almost like folk art to it where we're not exactly sure why some people are good at it But even having said that, we, it's wrong to say that we have no idea how these things work or what we can certainly describe what the difference is between one that fails and that's at least pretty good, you know? And so I think any ordinary person, if we can improve the user interface and improve the way it's taught any, any normal person that can learn even a tiny bit of programming like at a coding camp, making the turtle move around or something, we should be able to get to the point where they can understand basic machine learning as well. And we have to get there. All right in the future, I don't want it to be a black box. It doesn't need to be. >> Well basic machine learning is one thing, but how the machine made that decision is increasingly complex. Right? >> Not really it's not a matter of complexity. It's a funny thing. It's not exactly complexity. It has to do with getting a bunch of data from real people and then I'm massaging it and coming up with the right transformation so that the right thing spit out on the other side. And there's like a little, it's like to me it's a little bit more, it's almost like, I know this is going to sound strange but it's, it's almost like learning to dress like you take this data and then you dress it up in different ways and all of a sudden it turns functional in a certain way. Like if you get a bunch of people to tag, that's a cat, that's a dog. Now you have this big corpus of cats and dogs and now you want to tell them apart. You start playing with these different ways of working with it. That had been worked out. Maybe in other situations, you might have to tweak it a little bit, but you can get it to where it's very good. It can even be better than any individual person, although it's always based on the discrimination that people put into the system in the first place. In a funny way, it's like Yeah, it's like, it's like a cross between a democracy and a puppet show or something. Because what's happening is you're taking this data and just kind of transforming it until you find the right transformation that lets you get the right feedback loop with the original thing, but it's always based on human discrimination in the first place so it's not. It's not really cognition from first principles, it's kind of leveraging data, gotten from people and finding out the best way to do that and I think really, really work with it. You can start to get a two to feel for it. >> We're looking forward to seeing your results of that work Jared, thanks for coming on the cube. You're great guests. >> Really appreciate it >> I really appreciate you having me here. Good. Good luck to all of you. And hello out there in the land that those who are manipulated. >> Thanks again. The book last one, one last plug if I may. >> The book is 10 arguments for deleting your social media accounts right now and you might be watching this on one of them, so I'm about to disappear from your life if you take my advice. >> All right, thanks again. >> All right. Okay, keep it right there everybody. We'll be back with our next guest right after this short break. You're watching the cube from LiveWorx in Boston. We'll be right back. (upbeat music)

>> Live from San Francisco, it's theCUBE. Covering DockerCon '18. Brought to you by Docker and its ecosystem partners. >> Welcome back to theCUBE. We are live in San Francisco at DockerCon 2018. I'm Lisa Martin with John Troyer on a stunning day here in San Francisco. This event draws between 5,000 and 6,000 people in only its fifth year. They did a very good job during the general session this morning, John, of having some great female leaders on stage and we're very pleased to welcome another female leader to theCUBE for the first time. Alex Qin, you are the Director of Technology at Gakko. Welcome to theCUBE. >> Thank you, thank you. It's great to be here. >> So, you're speaking here at DockerCon 2018, I want to get to that in a second, but tell us a little bit about Gakko. What do you guys do? >> Um, yeah. So we're a global education design studio based in Tokyo and New York and what we do is we put on experimental education programs and build experimental education technology that aim to reclaim the magic of learning. So, we put on summer camps, we have coding classes, music classes and we build software for early learners. >> And by early learners what age group are you talking about? >> So ages three to five. What we build is beautiful story and art driven apps for kids ages three to five to be able to spend time more thoughtfully on tablets 'cause nowadays kids are always on tablets no matter what we do and so what we want to do is create a world that they can be in, in which parents feel like, this is a good place for my child to spend time. They're learning, it's artful, it's thoughtfully built. >> Great, well Alex you are also the founder of The Code Collective. >> The Code Cooperative, yes. >> The Code Cooperative, I'm sorry. How did you get started with that and can you tell us a little bit about that as well? >> Yes, so The Code Cooperative is my passion project and I started it in 2016, the day after the presidential elections actually, and it's an organization that teaches formerly incarcerated individuals computer literacy and coding, so that they can build websites and technical solutions to the problems they've identified in the criminal justice system. >> Some examples of that might be? >> A story I love to tell is from the pilot class. I had one student who was a 65 year old man and he'd been in prison for over 20 years and so at 65, he took our class and he learned HTML, CSS and JavaScript and built a website that aims to educate visitors about the legacy of slavery and Jim Crow in the criminal justice system today. Just like an interactive quiz. Yeah, that was really cool. It was called The Criminal Injustice System. >> Nice, nice. >> What were some of the drivers that really led you to go, you know what? We've got a huge opportunity here to take some of these people who have had made some different choices and really, sort of, rehabilitate them in a way that's gonna enable tech for good. What were some of those things that you just went, we've got to do this? >> That's a good question. Well, I read the book, The New Jim Crow, which you may have heard of. It's an incredible book that really details a lot of the problems that exist today within the U.S. criminal justice system and I thought to myself, I want to learn more about the justice system and contribute positively to justice system reform, but I don't know anything about it. So what I should do is work with people who have been through the system, learn from them and empower them to highlight the issues that they see within the justice system and that's something that I think is really important. When it comes to building technology, right now the gatekeepers of tech are kind of a homogenous group and we tend to build tech solutions for the entire world, but actually the people who are best equipped to solve problems are those who have experienced them and so that's why I decided to start The Code Cooperative. >> Nice. Alex, you're talking here, you've got an interesting titled session, I'll make sure I get it right, Shaving My Head Made Me a Better Programmer. If I can connect that to the rest of the DockerCon, maybe, I mean, Docker has been very good at their whole history about developer experience, making things easier for people and I think sometimes people don't realize not only when you make things easier, you actually can bring in new audiences. Kids, prisoners, right, are able to use today's technology where 30, 40 years ago they wouldn't have had access to it because it's easier, it's more powerful, it's more ubiquitous. But sometimes we get stuck in old tropes and so I'd love for you to kind of talk a little about your talk and kind of, what you're going to be talking about here at the show. >> Sure, yes. So, my talk is called Shaving My Head Made Me a Better Programmer and it's a little bit of a misleading title, but basically it's the story of my journey though the tech industry as a minority woman. So I studied computer science and I've been a software engineer for my entire career and yet, I've encountered a lot of challenges because of my gender, because of how I present to the world and when I shaved my head, a lot of those challenges kind of disappeared because I wasn't perceived as feminine anymore and so when I realized that tech isn't the meritocracy that I thought it was, I kind of started on this new quest to make tech as diverse and inclusive as possible so that people from all backgrounds, all genders can learn to code and write code happily and safely and it's just the story of how that happened and the lessons I've learned and some tips on how to make organizations more inclusive because that's the bulk of my work now. >> So you were a C.S. major in New York? >> Yes. >> So were you always interested in STEM as a kid or was it something that you got into when you were in college? What was that sort of age that you found it really exciting and said, no matter what, even if there's very few women here, I love this, I want to do this? >> That's a great question. So I am originally from France, actually. And when I was growing up there was really little computer science education in schools, but I really wanted to be an astronaut when I got to college so I joined the engineering program at my school and I'd never coded at that point, but one of the requirements was an intro to programming class in Python. So I took it and I fell in love with it immediately and I was like, I'm majoring in computer science, this is so cool, this is the coolest thing I've ever done and as I entered the computer science world I realized, oh, there's not that many women here and actually, I'm treated very differently. So, I fell in love with it and then because I love it so much I just kind of powered through. >> Your passion is very palpable, so at any point did you feel, sort of, out of place? Going, I'm one of the only females here, or did you say, I don't care, I like this. >> Yeah, it's both. I mean, you feel out of place when there's very few people who look like you in the room. Even if you don't want to feel out of place, even if you try to pretend that's not the case, you can't help but feel that and when I was starting out and throughout my career, people didn't necessarily want to work with me, didn't believe I was a good programmer, even though I was at the top of all my classes and so even though I tried to make the most out of my experience, I couldn't really escape the stigma attached to my gender in this field. >> Alex, we're at an interesting part of our culture now, I suppose, especially online. On one hand, social media has elevated a lot of folks' voices that would not have been heard otherwise because of gatekeepers. On the other hand, we have our current online discourse, which is kind of, not very pleasant sometimes. So I am interested both kind of how you're navigating that online and then maybe as a followup, then as you work with companies, how you're working with them and what you're telling them, but in terms of online, I love Twitter and yet it frustrates me. Facebook as well, et cetera. How do you navigate that online yourself? >> That's a great question. Honestly, I have been kind of retreating from social media. I haven't really experienced too many negative interactions on social media because I'm not really a big presence there. I did kind of have a really bad experience once during a Grace Hopper conference. I tweeted something during the Male Allied panel of like, 2015, or something and that got picked up by some GamerGate writers and then a lot of people started tweeting negative things at me, but that's kind of the extent of my negative experiences online. I do think that, as you say, social media has allowed for uplifting of voices that were previously unheard, has allowed for activism to organize. There's so many positive things that come from social media and also it has a really nefarious affect on people and I think that something needs to change in terms of how these companies build their software. It needs to be safer for all people and also needs to be built more ethically. Less trying to manipulate our psyches. >> That's, I think, super important. Luckily at least that's a conversation now, right Lisa? That at least Facebook, I think eventually as a society we'll, I hope, we'll get through this and figure this out, but I don't feel like we're particularly literate with social at this point. But I did want to ask about your work with companies. You said you do talk with some companies about diversity and things like that, is there any either signs that folks are getting it right or things that you start off with as you're working, if someone asks, how do we become a more diverse workforce? >> Yeah, that's a good question. I can't really point to any companies that, I say, are doing amazing. There are some companies where I know folks are very happy. Slack is one of them, thoughtbot is another one of them. I'll say Gakko, but a few tips I generally give organizations is that you need to work to understand the problem. Why is there a lack of diversity in tech? Why is your team not diverse? Then you need to measure your data. You can't make a positive change if you don't know how much you're changing, right? So gather diversity data on your team, not just in terms of who's there, but who's in a leadership role. Who gets promoted? Who gets fired? Who's a manager? And then you need to commit. That's, I think, the place where a lot of people struggle is there's a lot of candidates who fit this, kind of, homogenous image of what a programmer is and so it can be easy sometimes to be like, well we need to hire someone right now so let's just hire this person. But in order to actually make a change you need to commit and you need to say I'm not going to compromise on the goals that we've set. >> You're absolutely right, that commitment word is exactly what's needed to drive that accountability to hold organizations up to that. I was just at VMware a couple of weeks ago in Palo Alto at the Women Transforming Technology event and we had a whole day of all talking with females in tech, which I always loved to do and theCUBE is very passionate about supporting that. The cultural change is imperative. We talk about digital transformation at every event and there's the CIO that says, hey we have to change the culture here to transform digitally, but also to start moving those numbers from, what, less than 25% of tech roles are held by women. The culture has to change. It seems like you're in a position, potentially, to actually influence the culture at these companies that you talk to about opening their eyes to commit. Does that excite you from within? >> Yes, I do talk to a lot of organizations about this, but I think the work that I do that might actually tip the scale is, basically, the education programs that I run in New York. All of my classrooms reflect the diversity of New York, both in terms of student and teacher bodies. So all of my students learn in an environment that is extremely diverse. They learn from teachers who look like them and I wish I learned to code in that way. Another important thing we teach our students is how to code as an ethical endeavor. So we teach our students to measure the ethical ramifications of their decisions when they build software so that hopefully the technologists of tomorrow, the CTO's of tomorrow they build code in a way that is best for humanity. They build code with empathy. >> Goin' back to your day job. You're working with kids. We talked about getting through social media, cultural change. Its going to depend on the next generation. So Alex, are the kids alright? Are they gonna save us? >> The kids are pretty alright. I mean, so my classroom is basically coding meets social entrepreneurship so all of our kids build an app that solves a problem they've identified in their communities and these kids are just coming up with the most beautiful solutions, like, more brilliant than any adult that I've met. I feel good about the future. >> Well, it's key to get those different perspectives and when you were saying, they're having the opportunity to code and create apps that are relevant to them that's where you can really ignite that passion. >> Exactly, that's so important >> It is important because when you're passionate about something, and we saw that on stage today with a lot of the Docker folks and Microsoft and McKesson, when you're passionate about something and really making a change, you can feel it. So it's good to hear that we're going in the right direction. Also, we're in this age, you talked about ethics, where it's essential. Because technology, we see a lot of examples where tech is not used for good and there's world leaders getting some of the leaders of tech companies together saying, I'm challenging you, make tech for good because we're seeing too much of the negative right now. How does that influence, whether it's the breaches at Equifax, or, there was a breach recently at MyHeritage, the DNA testing companies, to Cambridge Analytica. How do you see the kids, the young kids respond to that, going, that's a really poor use of tech. Are they aware of that? >> I think some kids are and in our classroom we spend some time talking about, we have discussions about, ethics of software. So that's something that's very important to us. But largely, most classrooms in the United States, no, I mean computer science education is not a standard in most classrooms in the US. In New York state, only 1% of high schoolers actually have access to any kind of computer science education and so most kids, they might hear tid bits from the T.V. or social media or something, but they're not necessarily informed enough to make one, good decisions as consumers and two, good decisions as potential technologists. So that's something that we are trying to spread and I hope other folks are also trying to work on. >> Another thing that I think is shocking is when we were at the Women Transforming Technology event just a few weeks ago at VMware in Palo Alto, they just announced with Stanford, Stanford is investing 15 million dollars into their gender research. VMware and Stanford wanting to look at what are the barriers for women in tech and minorities in tech and starting to dissolve some of those barriers. One of the things they actually had in their press release announcing this big 15 million dollar investment from VMware and Stanford is a Mckinsey report that said 20%, sorry, enterprise organizations that have females in management positions, probably executive management positions, didn't specify positions, are 20% more profitable. You just think, the numbers are saying when you have more thought diversity, you're actually going to be a more profitable organization, but I think to your point earlier, Alex, there has to be a commitment and there has to be a group within an organization that stands accountable. >> Absolutely. >> So we are thankful for you. (Alex laughs) for donating some of your time today to tell us what you're doing, it's good to hear the next generation, John, I think they got our backs. >> Alright, that's good. >> And Alex, have a great time with your very provocative session this afternoon. >> Thank you. >> We thank you so much for your time and it's really cool to hear how you're using your passion for tech for good. >> Thank you so much, it was great to be here. >> We want to thank you for watching theCUBE. I'm Lisa Martin with John Troyer. From San Francisco at DockerCon 2018. Stick around, John and I will be right back with out next guest. (upbeat music)

>> Announcer: From San Mateo, California, it's theCUBE! Covering SnapLogic, Innovation Day, 2018. Brought to you by SnapLogic. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are in San Mateo, at what they call the crossroads, it's 92 and 101. If you're coming by and probably sitting in a traffic, look up and you'll see SnapLogic. It's their new offices. We're really excited to be here for Innovation Day. We're excited to have this CTO, James Markarian. James, great to see you and I guess, we we last talked was a couple years ago in New York City. >> Yeah that's right, and why was I there? It was like a big data show. >> That's right. >> And we we are two years later talking about big data. >> Big data, big data is fading a little bit, because now big data is really an engine, that's powering this new thing that's so exciting, which is all about analytics, and machine learning, and we're going to eventually stop saying artificial intelligence and say augmented intelligence, 'cause there's really nothing artificial about it. >> Yeah and we might stop saying big data and just talk about data because it's becoming so ubiquitous. >> Jeff: Right. >> I know that big data, it's not necessarily going away but it's sort of how we're thinking about handling it is, like kind of evolved over time, especially in the last couple of years. >> Right. >> That's what we're kind of seeing from our customers. >> 'Cause there's kind of an ingredient now, right? It's no longer this new shiny object now. It's just part of the infrastructure that helps you get everything else done. >> Yeah, and I think when you think about it, from like, an enterprise point of view, that that shift is going from experimentation to operationalizing. I think that the things you look for in experimentation, there's like, one set of things here looking for proving out the overall value, regardless maybe of cost and uptime and other things and as you operationalize you start thinking about other considerations that obviously Enterprise IT has to think about. >> Right, so if you think back to like, Hadoop Summit and Hadoop World who were first cracking their teeth, like in 2010 or around that time frame, one of the big discussions that always comes up and that was before kind of the rise of public cloud, you know which has really taken off over the last several years, there's this kind of ongoing debate between, do you move the data to the compute or do you move the compute to the data? There was always like, this monster data gravity issue which was almost insurmountable and many would say, oh, you're never going to get all your data into the cloud. It's just way too hard and way too expensive. But, now Amazon has Snowball and Snowball isn't big enough. They actually had a diesel truck that'll come and help you come move your data. Amazon rolled that thing across the stage a couple of years ago. The data gravity thing seems to be less and if you think of a world with infinite compute, infinite stored, infinite networking asyndetically approaching zero, not necessarily good news for some vendors out there but that's a world that we're eventually getting to that changes the way that you organize all this stuff. >> Yeah, I think so and so much has changed. I was fortunate to be one of the early speakers, like I used to do Worlds and everything, and I was adamantly proclaiming you know, the destiny of Hadoop as bright and shiny and there's this question about what really happened. I think that there's a kind of a few different variables that kind of shifted at the same time. One, is of course, this like glut of computing in the cloud happened and there are so many variables moving at once. It's like, How much time do you have Jeff? >> Ask them to get a couple more drinks for us. >> Seeing our lovely new headquarters here and one of the things is that there is no big data center. We have a little closet with some of the servers we keep around but mostly, everything we do is on Amazon. You're even looking at things like, commercial real estate is changing because I don't need all the cooling and the power and the space for my data center that I once had. >> Jeff: Right, right. >> I become a lot more space efficient than I used to be and so the cloud is really kind of changing everything. On the data side, you mention this like, interesting philosophical shift, going from I couldn't possibly do it in the cloud to why in the world would we not do things in the cloud. Maybe the one stall word in there being some fears about security. Obviously there's been a lot of breaches. I think that there's still a lot of introspection everyone needs to do about, are my on premise systems actually more secure than some of these cloud providers? It's really not clear that we know the answer to that. In fact, we suspect that some of the cloud providers are actually more secure because they are professionals about it and they have the best practice. >> And a whole lot of money. >> The other thing that happened that you didn't mention, that's approaching infinity and we're not quite there yet, is interconnect speeds. So it used to be the case that I have a bunch of mainframes and I have a tier rating system and I have a high speed interconnect that puts the two together. Now with fiber networks and just in general, you can run super high speed, like WAN. Especially if you don't care quite as much about latency. So if 500 millisecond latency is still okay with you. >> Great. >> You can do a heck of a lot and move a lot to the cloud. In fact, it's so good, that we went from worrying, could I do this in the cloud at all to well, why wouldn't I do somethings in Amazon and some things in Microsoft and some things in Google? Even if it meant replicating my data across all these environments. The backdrop for some of that is, we had a lot of customers and I was thinking that people would approach it this way, they would install on premise Hadoop, whether it's like Apache or Cloud Air or the other vendors and I would hire a bunch of folks that are the administrators and retire terra data and I'm going to put all my ETL jobs on there, etc. It turned out to be a great theory and the practice is real for some folks but it turned out to be moving a lot of things to kind of shifting sands because Hadoop was evolving at the time. A lot of customers were putting a lot of pressure on it, operational pressure. Again, moving from experimentation phase over to like, operational phase. >> Jeff: Right, right. >> When you don't have the uptime guarantee and I can't just hire somebody off the street to administer this, it has to be a very sharp, knowledgeable person that's very expensive, people start saying, what am I really getting from this and can I just dump it all in S3 and apply a bunch of technology there and let Amazon worry about keeping this thing up and running? People start to say, I used to reject that idea and now it's sounding like a very smart idea. >> It's so funny we talk about people processing tech all the time, right? But they call them tech shows, they don't call them people in process shows. >> Right. >> At least not the ones we go to but time and time again I remember talking to some people about the Hadoop situation and there's just like, no Hadoop people. Sometimes technology all day long. There just aren't enough people with the skills to actually implement it. It's probably changed now but I remember that was such a big problem. It's funny you talk about security and cloud security. You know, at AWS, on Tuesday night of Reinvent, they have a special, kind of a technical keynote speak and like, James Hamilton would go. In the amount of resources, and I just remember one talk he gave just on their cabling across the ocean, and the amount of resources that he can bring to bear, relative to any individual company, is so different; much less a mid-tier company or a small company. I mean, you can bring so much more resources, expertise and knowledge. >> Yeah, the economy is a scale, their just there. >> They're just crazy. >> That's right and that why you know, you sort of assume that the cloud sort of, eventually eats everything. >> Right, right. >> So there's no reason to believe this won't be one of those cases. >> So you guys are getting Extreme. So what is Snaplogic Extreme? >> Well, Snaplogic Extreme is kind of like a response to this trend of data moving from on premise to the cloud and there are some interesting dynamics of that movement. First of all, you need to get data into the cloud, first of all and we've been doing that for years. Connect to everything, dump it in S3, ADLS, etc. No problem. The thing we're seeing with cloud computing is like, there's another interesting shift. Not only is it kind of like mess for less, and let Amazon manage all this, and I probably refer to Amazon more than other vendors would appreciate. >> Right, right. They're the leaders so let's call a spade a spade. >> Yeah. >> Certainly Google and Microsoft are out there as well so those are the top three and we've acknowledged that. >> One of the interesting things about it is that you couldn't really adequately achieve on premises is the burstiness of your compute. I run at a steady state where I need, you know, 10 servers or a 100 servers, but every once in a while, I need like, 1,000 or 10,000 servers to apply to something. So what's the on premise model? Rack and stack, 10,000 machines, and it's like waiting for the great pumpkin, waiting for that workload to come that I've been waiting months and months for and maybe it never comes but I've been paying for it. I paid for a software license for the thing that I need to run there. I'm paying for the cabling and the racking and everything and the person administering. Make sure the disks are all operating in the case where it gets used. Now, all of a sudden, we are taking Amazon and they're saying, hey, pay us for what you're using. You can use reserved pricing and pay a lower rate for the things you might actually care about on a consistent basis but then I'm going to allow you to spike, and I'll just run the meter. So this has caused software vendors like us, to look at the way we charge and the way that we deploy our resources and say, hey, that's a very good model. We want to follow that and so we introduced Snaplogic Extreme, which has a few different components. Basically, it enables us to operate in these elastic environments, shift our thinking in pricing so that we don't think about like, node based or god forbid, core based pricing and say like, hey, basically pay us for what you do with your data and don't worry about how many servers it's running on. Let Snaplogic worry about spinning up and spinning down these machines because a lot of these workloads are data integration or application workloads that we know lots about. >> Right. >> So first of all, we manage these ephemeral, what we call ephemeral or elastic clusters. Second of all, the way that we distribute our workload is by generating Spark code currently. We use the same graphic environment that you use for everything but instead of running on our engines, we kind of spit out Spark code on the end that takes advantage of the massive scale out potential for these ephemeral environments. >> Right. >> We've also kind of built this in such a way that it's Spark today but it could be like, Native or some other engine like Flank or other things that come up. We really don't care like what back end engine actually is as long as it can run certain types of data oriented jobs. It's actually like lots of things in one. We combine out data acquisition and distribution capability with this like, massive elastic scale out capability. >> Yeah, it's unbelievable how you can spin that up and then of course, most people forget you need to spin it down after the event. >> James: Yeah, that's right. >> We talked to a great vendor who talked about, you know, my customer spends no money with me on the weekend, zero. >> James: Right. >> And I'm thrilled because they're not using me. When they do use me, then they're buying stuff. I think what's really interesting is how that changes. Also, your relationship with your customer. If you have a recurring revenue model, you have to continue to deliver a value. You have to stay close to your customer. You have to stay engaged because it's not a one time pop and then you send them the 15% or 20% maintenance bill. It's really this ongoing relationship and they're actually gaining value from your products each and every time you use that. It's a very different way. >> Yeah, that's right. I think it creates better relationships because you feel like, what we do is unproportionate to what they do and vise versa, so it has this fundamental fairness about it, if you will. >> Right, it's a good relationship but I want to go down another path before you turn the cameras on. Talk a little bit about the race always between the need for compute and the compute. It used to be personified best with Microsoft and Intel until we come out with a new chip and then Microsoft OS would eat up all the extra capacity and then they'd come up with a new chip and it was an ongoing thing. You made an interesting comment that, especially in the cloud world where the scale of these things is much, much bigger, that ran a world now where the compute and the storage have kind of, outpaced the applications, if you will, and there's an opportunity for the application to catch up. Oh by the way, we have this cool new thing called machine learning and augmented intelligence. I wonder if you could, is that what's going to fill or kind of rebalance the consumption pattern? >> Yeah, it seems that way and I always think about kind of like, compute and software spiraling around each other like a helix. >> Like at one point, one is leading the other and they sort of just, one eventually surpasses the other and then you need innovation on the other side. I think for a while, like if you turn the clock way back to like, when the Pentium was introduced and everyone was like, how are we ever going to use all of the compute power. >> Windows 95, whoo! >> You know, power of like the Pentium. Do I really need to run my spreadsheets 100% faster? There's no business value whatsoever in transacting faster, or like general user interface or like graphical user interfaces or rendering web pages. Then you start seeing this new glut, often led by like researchers first. Like, software applications coming up that use all of this power because in academia you can start saying, what if I did have infinite compute? What would I do differently? You see things, you know like VR and advanced gaming, come up on the consumer side. Then I think the real answer on the business side is AI and ML. The general trend I start thinking of is something I used to talk about, back in the old days, which is conversion of like, having machines work for us instead of us working for machines. The only way we're ever going to get there is by having higher and higher intelligence on the application side so that it kind of intuits more based on what it's seen before and what it knows about you, etc., in terms of the task that needs to get done. Then there's this whole new breed of person that you need in order to wield all that power because like Hadoop, it's not just natural. You don't just have people floating around like, hey, you know, I'm going to be an Uzi expert or a yarn expert. You don't run into people everyday that's like, oh, yeah, I know neural nets well. I'm a gradient descent expert or whatever you're model is. It's really going to drive like, lots of changes I think. >> Right, well hopefully it does and especially like we were talking about earlier, you know, within core curriculums at schools and stuff. We were with Grace Hopper and Brenda Wilkerson, the new head of the Anita Borg organization, was at this Chicago public school district and they're actually starting to make CS a requirement, along with biology and and physics and chemistry and some of these other things. >> Right. >> So we do have a huge, a huge dearth of that but I want to just close out on one last concept before I let you go and you guys are way on top of this. Greg talked about what you just talked about, which is making the computers work for us versus the other way around. That's where the democratization of the power that we heard a lot about the democratization of big data and the tools and now you guys you guys are talking about the democratization of the integration, especially when you have a bunch of cloud based applications that everybody has access to and maybe, needs to stitch together a different way. But when you look at this whole concept of democratization of that power, how do you see that kind of playing out over the next several years? >> Yeah, that's a very big- >> Sorry I didn't bring you a couple of beer before I brought that up. >> Oh no, I got you covered. So it's a very big, interesting question because I think that you know, first of all, it's one of these, god knows, we can't predict with a lot of accuracy how exactly that's going to look because we're sort of juxtaposing two things. One is, part of the initial move to the cloud was the failure to properly democratize data inside the enterprise, for whatever reason, and we didn't do it. Now we have the computer resources and the central, kind of web based access to everything. Great. Now we have Cambridge Analytica and like, Facebook and people really thinking about data privacy and the fact that we want ubiquitous safe access. I think we know how to make things ubiquitous. The question is, do we know how to make it safe and fair so that the right people are using the right data and the right way? It's a little bit like, you know, there's all these cautionary tales out there like, beware of AI and robotics and everything and nobody really thinks about the danger of the data that's there. It's a much more immediate problem and yet it's sort of like the silent killer until some scandal comes up. We start thinking about these different ways we can tackle it. Obviously there's great solutions for tokenization and encryption and everything at the data level but even if you have the access to it, the question is, how do you control that wildfire that could happen as soon as the horse leaves the barn. Maybe not in it's current form, but when you look at things like Blockchain, there's been a lot of predictions about how Blockchain can be used around like, data. I think that this privacy and this curation and tracking of who has the data, who has access to it and can we control it, I think you are looking at even more like, centralized and guarded access to this private data. >> Great, interesting times. >> Yeah, yeah Jeff, for sure. >> Alright James, well thanks for taking a couple of minutes with us. I really enjoyed the conversation. >> Yeah, it's always great. Thanks for having me Jeff. >> It's James on Jeff and you're watching theCUBE We're at the Snaplogic headquarters in San Mateo, California and thanks for watching. (electronic music)

>> Announcer: From San Mateo, California, it's theCUBE! Covering SnapLogic, Innovation Day, 2018. Brought to you by SnapLogic. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are in San Mateo, at what they call the crossroads, it's 92 and 101. If you're coming by and probably sitting in a traffic, look up and you'll see SnapLogic. It's their new offices. We're really excited to be here for Innovation Day. We're excited to have this CTO, James Markarian. James, great to see you and I guess, we we last talked was a couple years ago in New York City. >> Yeah that's right, and why was I there? It was like a big data show. >> That's right. >> And we we are two years later talking about big data. >> Big data, big data is fading a little bit, because now big data is really an engine, that's powering this new thing that's so exciting, which is all about analytics, and machine learning, and we're going to eventually stop saying artificial intelligence and say augmented intelligence, 'cause there's really nothing artificial about it. >> Yeah and we might stop saying big data and just talk about data because it's becoming so ubiquitous. >> Jeff: Right. >> I know that big data, it's not necessarily going away but it's sort of how we're thinking about handling it is, like kind of evolved over time, especially in the last couple of years. >> Right. >> That's what we're kind of seeing from our customers. >> 'Cause there's kind of an ingredient now, right? It's no longer this new shiny object now. It's just part of the infrastructure that helps you get everything else done. >> Yeah, and I think when you think about it, from like, an enterprise point of view, that that shift is going from experimentation to operationalizing. I think that the things you look for in experimentation, there's like, one set of things here looking for proving out the overall value, regardless maybe of cost and uptime and other things and as you operationalize you start thinking about other considerations that obviously Enterprise IT has to think about. >> Right, so if you think back to like, Hadoop Summit and Hadoop World who were first cracking their teeth, like in 2010 or around that time frame, one of the big discussions that always comes up and that was before kind of the rise of public cloud, you know which has really taken off over the last several years, there's this kind of ongoing debate between, do you move the data to the compute or do you move the compute to the data? There was always like, this monster data gravity issue which was almost insurmountable and many would say, oh, you're never going to get all your data into the cloud. It's just way too hard and way too expensive. But, now Amazon has Snowball and Snowball isn't big enough. They actually had a diesel truck that'll come and help you come move your data. Amazon rolled that thing across the stage a couple of years ago. The data gravity thing seems to be less and if you think of a world with infinite compute, infinite stored, infinite networking asyndetically approaching zero, not necessarily good news for some vendors out there but that's a world that we're eventually getting to that changes the way that you organize all this stuff. >> Yeah, I think so and so much has changed. I was fortunate to be one of the early speakers, like I used to do Worlds and everything, and I was adamantly proclaiming you know, the destiny of Hadoop as bright and shiny and there's this question about what really happened. I think that there's a kind of a few different variables that kind of shifted at the same time. One, is of course, this like glut of computing in the cloud happened and there are so many variables moving at once. It's like, How much time do you have Jeff? >> Ask them to get a couple more drinks for us. >> Seeing our lovely new headquarters here and one of the things is that there is no big data center. We have a little closet with some of the servers we keep around but mostly, everything we do is on Amazon. You're even looking at things like, commercial real estate is changing because I don't need all the cooling and the power and the space for my data center that I once had. >> Jeff: Right, right. >> I become a lot more space efficient than I used to be and so the cloud is really kind of changing everything. On the data side, you mention this like, interesting philosophical shift, going from I couldn't possibly do it in the cloud to why in the world would we not do things in the cloud. Maybe the one stall word in there being some fears about security. Obviously there's been a lot of breaches. I think that there's still a lot of introspection everyone needs to do about, are my on premise systems actually more secure than some of these cloud providers? It's really not clear that we know the answer to that. In fact, we suspect that some of the cloud providers are actually more secure because they are professionals about it and they have the best practice. >> And a whole lot of money. >> The other thing that happened that you didn't mention, that's approaching infinity and we're not quite there yet, is interconnect speeds. So it used to be the case that I have a bunch of mainframes and I have a tier rating system and I have a high speed interconnect that puts the two together. Now with fiber networks and just in general, you can run super high speed, like WAN. Especially if you don't care quite as much about latency. So if 500 millisecond latency is still okay with you. >> Great. >> You can do a heck of a lot and move a lot to the cloud. In fact, it's so good, that we went from worrying, could I do this in the cloud at all to well, why wouldn't I do somethings in Amazon and some things in Microsoft and some things in Google? Even if it meant replicating my data across all these environments. The backdrop for some of that is, we had a lot of customers and I was thinking that people would approach it this way, they would install on premise Hadoop, whether it's like Apache or Cloud Air or the other vendors and I would hire a bunch of folks that are the administrators and retire terra data and I'm going to put all my ETL jobs on there, etc. It turned out to be a great theory and the practice is real for some folks but it turned out to be moving a lot of things to kind of shifting sands because Hadoop was evolving at the time. A lot of customers were putting a lot of pressure on it, operational pressure. Again, moving from experimentation phase over to like, operational phase. >> Jeff: Right, right. >> When you don't have the uptime guarantee and I can't just hire somebody off the street to administer this, it has to be a very sharp, knowledgeable person that's very expensive, people start saying, what am I really getting from this and can I just dump it all in S3 and apply a bunch of technology there and let Amazon worry about keeping this thing up and running? People start to say, I used to reject that idea and now it's sounding like a very smart idea. >> It's so funny we talk about people processing tech all the time, right? But they call them tech shows, they don't call them people in process shows. >> Right. >> At least not the ones we go to but time and time again I remember talking to some people about the Hadoop situation and there's just like, no Hadoop people. Sometimes technology all day long. There just aren't enough people with the skills to actually implement it. It's probably changed now but I remember that was such a big problem. It's funny you talk about security and cloud security. You know, at AWS, on Tuesday night of Reinvent, they have a special, kind of a technical keynote speak and like, James Hamilton would go. In the amount of resources, and I just remember one talk he gave just on their cabling across the ocean, and the amount of resources that he can bring to bear, relative to any individual company, is so different; much less a mid-tier company or a small company. I mean, you can bring so much more resources, expertise and knowledge. >> Yeah, the economy is a scale, their just there. >> They're just crazy. >> That's right and that why you know, you sort of assume that the cloud sort of, eventually eats everything. >> Right, right. >> So there's no reason to believe this won't be one of those cases. >> So you guys are getting Extreme. So what is Snaplogic Extreme? >> Well, Snaplogic Extreme is kind of like a response to this trend of data moving from on premise to the cloud and there are some interesting dynamics of that movement. First of all, you need to get data into the cloud, first of all and we've been doing that for years. Connect to everything, dump it in S3, ADLS, etc. No problem. The thing we're seeing with cloud computing is like, there's another interesting shift. Not only is it kind of like mess for less, and let Amazon manage all this, and I probably refer to Amazon more than other vendors would appreciate. >> Right, right. They're the leaders so let's call a spade a spade. >> Yeah. >> Certainly Google and Microsoft are out there as well so those are the top three and we've acknowledged that. >> One of the interesting things about it is that you couldn't really adequately achieve on premises is the burstiness of your compute. I run at a steady state where I need, you know, 10 servers or a 100 servers, but every once in a while, I need like, 1,000 or 10,000 servers to apply to something. So what's the on premise model? Rack and stack, 10,000 machines, and it's like waiting for the great pumpkin, waiting for that workload to come that I've been waiting months and months for and maybe it never comes but I've been paying for it. I paid for a software license for the thing that I need to run there. I'm paying for the cabling and the racking and everything and the person administering. Make sure the disks are all operating in the case where it gets used. Now, all of a sudden, we are taking Amazon and they're saying, hey, pay us for what you're using. You can use reserved pricing and pay a lower rate for the things you might actually care about on a consistent basis but then I'm going to allow you to spike, and I'll just run the meter. So this has caused software vendors like us, to look at the way we charge and the way that we deploy our resources and say, hey, that's a very good model. We want to follow that and so we introduced Snaplogic Extreme, which has a few different components. Basically, it enables us to operate in these elastic environments, shift our thinking in pricing so that we don't think about like, node based or god forbid, core based pricing and say like, hey, basically pay us for what you do with your data and don't worry about how many servers it's running on. Let Snaplogic worry about spinning up and spinning down these machines because a lot of these workloads are data integration or application workloads that we know lots about. >> Right. >> So first of all, we manage these ephemeral, what we call ephemeral or elastic clusters. Second of all, the way that we distribute our workload is by generating Spark code currently. We use the same graphic environment that you use for everything but instead of running on our engines, we kind of spit out Spark code on the end that takes advantage of the massive scale out potential for these ephemeral environments. >> Right. >> We've also kind of built this in such a way that it's Spark today but it could be like, Native or some other engine like Flank or other things that come up. We really don't care like what back end engine actually is as long as it can run certain types of data oriented jobs. It's actually like lots of things in one. We combine out data acquisition and distribution capability with this like, massive elastic scale out capability. >> Yeah, it's unbelievable how you can spin that up and then of course, most people forget you need to spin it down after the event. >> James: Yeah, that's right. >> We talked to a great vendor who talked about, you know, my customer spends no money with me on the weekend, zero. >> James: Right. >> And I'm thrilled because they're not using me. When they do use me, then they're buying stuff. I think what's really interesting is how that changes. Also, your relationship with your customer. If you have a recurring revenue model, you have to continue to deliver a value. You have to stay close to your customer. You have to stay engaged because it's not a one time pop and then you send them the 15% or 20% maintenance bill. It's really this ongoing relationship and they're actually gaining value from your products each and every time you use that. It's a very different way. >> Yeah, that's right. I think it creates better relationships because you feel like, what we do is unproportionate to what they do and vise versa, so it has this fundamental fairness about it, if you will. >> Right, it's a good relationship but I want to go down another path before you turn the cameras on. Talk a little bit about the race always between the need for compute and the compute. It used to be personified best with Microsoft and Intel until we come out with a new chip and then Microsoft OS would eat up all the extra capacity and then they'd come up with a new chip and it was an ongoing thing. You made an interesting comment that, especially in the cloud world where the scale of these things is much, much bigger, that ran a world now where the compute and the storage have kind of, outpaced the applications, if you will, and there's an opportunity for the application to catch up. Oh by the way, we have this cool new thing called machine learning and augmented intelligence. I wonder if you could, is that what's going to fill or kind of rebalance the consumption pattern? >> Yeah, it seems that way and I always think about kind of like, compute and software spiraling around each other like a helix. >> Like at one point, one is leading the other and they sort of just, one eventually surpasses the other and then you need innovation on the other side. I think for a while, like if you turn the clock way back to like, when the Pentium was introduced and everyone was like, how are we ever going to use all of the compute power. >> Windows 95, whoo! >> You know, power of like the Pentium. Do I really need to run my spreadsheets 100% faster? There's no business value whatsoever in transacting faster, or like general user interface or like graphical user interfaces or rendering web pages. Then you start seeing this new glut, often led by like researchers first. Like, software applications coming up that use all of this power because in academia you can start saying, what if I did have infinite compute? What would I do differently? You see things, you know like VR and advanced gaming, come up on the consumer side. Then I think the real answer on the business side is AI and ML. The general trend I start thinking of is something I used to talk about, back in the old days, which is conversion of like, having machines work for us instead of us working for machines. The only way we're ever going to get there is by having higher and higher intelligence on the application side so that it kind of intuits more based on what it's seen before and what it knows about you, etc., in terms of the task that needs to get done. Then there's this whole new breed of person that you need in order to wield all that power because like Hadoop, it's not just natural. You don't just have people floating around like, hey, you know, I'm going to be an Uzi expert or a yarn expert. You don't run into people everyday that's like, oh, yeah, I know neural nets well. I'm a gradient descent expert or whatever you're model is. It's really going to drive like, lots of changes I think. >> Right, well hopefully it does and especially like we were talking about earlier, you know, within core curriculums at schools and stuff. We were with Grace Hopper and Brenda Wilkerson, the new head of the Anita Borg organization, was at this Chicago public school district and they're actually starting to make CS a requirement, along with biology and and physics and chemistry and some of these other things. >> Right. >> So we do have a huge, a huge dearth of that but I want to just close out on one last concept before I let you go and you guys are way on top of this. Greg talked about what you just talked about, which is making the computers work for us versus the other way around. That's where the democratization of the power that we heard a lot about the democratization of big data and the tools and now you guys you guys are talking about the democratization of the integration, especially when you have a bunch of cloud based applications that everybody has access to and maybe, needs to stitch together a different way. But when you look at this whole concept of democratization of that power, how do you see that kind of playing out over the next several years? >> Yeah, that's a very big- >> Sorry I didn't bring you a couple of beer before I brought that up. >> Oh no, I got you covered. So it's a very big, interesting question because I think that you know, first of all, it's one of these, god knows, we can't predict with a lot of accuracy how exactly that's going to look because we're sort of juxtaposing two things. One is, part of the initial move to the cloud was the failure to properly democratize data inside the enterprise, for whatever reason, and we didn't do it. Now we have the computer resources and the central, kind of web based access to everything. Great. Now we have Cambridge Analytica and like, Facebook and people really thinking about data privacy and the fact that we want ubiquitous safe access. I think we know how to make things ubiquitous. The question is, do we know how to make it safe and fair so that the right people are using the right data and the right way? It's a little bit like, you know, there's all these cautionary tales out there like, beware of AI and robotics and everything and nobody really thinks about the danger of the data that's there. It's a much more immediate problem and yet it's sort of like the silent killer until some scandal comes up. We start thinking about these different ways we can tackle it. Obviously there's great solutions for tokenization and encryption and everything at the data level but even if you have the access to it, the question is, how do you control that wildfire that could happen as soon as the horse leaves the barn. Maybe not in it's current form, but when you look at things like Blockchain, there's been a lot of predictions about how Blockchain can be used around like, data. I think that this privacy and this curation and tracking of who has the data, who has access to it and can we control it, I think you are looking at even more like, centralized and guarded access to this private data. >> Great, interesting times. >> Yeah, yeah Jeff, for sure. >> Alright James, well thanks for taking a couple of minutes with us. I really enjoyed the conversation. >> Yeah, it's always great. Thanks for having me Jeff. >> It's James on Jeff and you're watching theCUBE We're at the Snaplogic headquarters in San Mateo, California and thanks for watching. (electronic music)

>> Narrator: Live, from downtown San Francisco. It's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. (techno music) >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. And we are here at the IBM CDO Strategy Summit hashtag IBMCDO. Caitlin Halferty is here. She's a client engagement executive for the chief data officer at IBM. Caitlin great to see you again. >> Great to be here, thank you. >> And she's joined by Brandon Purcell, who's principal analyst at Forrester Research. Good to have you on. >> Thanks very much, thanks for having me. >> First time on theCUBE. >> Yeah. >> You're very welcome. >> I'm a newbie. >> Caitlin... that's right, you're a newbie. You'll be a Cube alum in no time, I promise you. So Caitlin let's start with you. This is, you've done a number of these CDO events. You do some in Boston, you do some in San Francisco. And it's really great to see the practitioners here. You guys are bringing guys like Inderpal to the table. You've announced your blueprint in it. The audience seems to be lapping up the knowledge transfer. So what's the purpose of these events? How has it evolved? And just set the table for us. >> Sure, so we started back in 2014 with our first Chief Data Officer Summit and we held that here in San Francisco. Small group, probably only had about 30 or 40 attendees. And we said let's make this community focused, peer to peer networking. We're all trying to, ya know, build the role of either the Chief Data Officer or whomever is responsible for enterprise wide data strategy for their company, a variety of different titles. And we've grown that event over, since 2014. We do Spring, in San Francisco, which tends to be a bit more on the technical side, given where we are here in San Francisco in Silicon Valley. And then we do our business focused sessions in Fall in Boston. And I have to say, it's been really nice to see the community grow from a small set of attendees. And now was are at about 130 that join us on each coast. So we've built a community in total of about 500 CDOs and data executives, >> Nice. that are with us on this journey, so they're great. >> And Brandon, your focus at Forrester, part of it is AI, I know you did some other things in analytics, the ethics of AI, which we're going to talk about. I have to ask you from Forrester's perspective, we're enter... it feels like we're entering this new era of there's digital, there's data, there's AI. They seem to all overlap. What's your point of view on all this? >> So, I'm extremely optimistic about the future of AI. I realize that the term artificial intelligence is incredibly hyped right now. But I think it will ultimately fulfill it's promise. If you think about the life cycle of analytics, analytics start their lives as customer data. As customers interact and transact with you, that creates a foot print that you then have to analyze to unleash some sort of insight. This customer's likely to buy, or churn, or belongs to a specific segment. Then you have to take action. The buzzwords of the past have really focused on one piece of that life cycle. Big data, the data piece. Not much value unless you analyze that. So then predictive analytics, machine learning. What AI promises to do is to synthesize all of those pieces, from data, to insights, to action. And continuously learn and optimize. >> It's interesting you talk about that in terms of customer churn. I mean, with the internet, there was like a shift in the balance of power to the consumer. There used to be that the brand had all the knowledge about the buyer. And then with the internet, we shop around, we walk into a store and, look at them. Then we go buy it on the internet right? Now that AI maybe brings back more balance, symmetry. I mean, what are your thoughts on that? Are the clients that you work with, trying to sort of regain that advantage? So they can better understand the customer. >> Yeah, well that's a great question. I mean, if there's one kind of central ethos to Forrester's research it's that we live in the age of the customer and understanding and anticipating customer needs is paramount to be able to compete, right? And so it's the businesses in the age of AI and the age of the customer that have the data on the customer and enable the ability to distill that into insights that will ultimately succeed. And so the companies that have been able to identify the right value exchange with consumers, to give us a sense of convenience, so that we're willing to give up enough personal data to satisfy that convenience are the ones that I think are doing well. And certainly Netflix and Amazon come to mind there. >> Well for sure, and of course that gets into the privacy and the ethics of AI. I mean everyone's making a big deal out of this. You own your data. >> Yeah. >> You're not trying to monetize, ya know, figure out which ad to click on. Maybe give us your perspective, Caitlin, on IBMs point of view there? >> Sure, so we lead with this thought around trusting your data. You're data's your data. Insights derive from that data, your insights. We spend a lot of time with our Watson Legal folks. And one of the things, pieces of material we've released today is the real detail at every level how you engage the traceability of where your data is. So you have a sense of confidence that you know how it's treated, how it's curated. If it's used in some third party fashion. The ability to know that, have visibility into it. The opt-out, opt-in opt-out set of choices. Making sure that we're not exploiting the network effect, where perhaps party C benefits from data exchange between A and B. That A and B do not, or do not have an opportunity to influence. And so what we wanted to do, here at the summit over the next couple of days is really share that in detail and our thoughts around it. And it comes back to trust and being able to have that viability and traceability of your data through the value chain. >> So of course Brandon, as a customer I'm paying IBM so I would expect that IBM would look out for my privacy and make that promise. I don't really pay Facebook right? But I get some value out of it. So what are the ethics of that? Is it a pay or no pay? Or is it a value or no value? Is it everybody really needs to play by the same rules? How to you parse all that? >> Ya know, I hate to use a vague term. But it's a reasonable expectation. Like I think that when a person interacts with Facebook, there is a reasonable expectation that they're not going to take that data and sell it or monetize it to some third party, like Cambridge Analytica. And that's where they dropped the ball in that case. But, that's just in the actual data collection itself. There's also, there are also inherent ethical issues in how the data is actually transformed and analyzed. So just because you don't have like specific characteristics or attributes in data, like race and gender and age and socioeconomic status, in a multidimensional data set there are proxies for those through something called redundant encoding. So even if you don't want to use those factors to make decisions, you have to be very careful because they're probably in there anyway. And so you need to really think about what are your values as a brand? And when can you actually differentiate treatment, based on different attributes. >> Because you can make accurate inferences from that. >> Brandon: Yeah you're absolutely (mumbles). >> And is it the case of actually acting on that data? Or actually the ability to act on that data? If that makes sense to you. In other words, if an organization has that data and could, in theory, make the inference, but doesn't. Is that crossing the line? Is it the responsibility of the organization to identify those exposures and make sure that they can not be inferred? >> Yeah, I think it is. I think that that is incumbent upon our organizations today. Eventually regulators are going to get around to writing rules around this. And there's already some going into effect of course in Europe, with GDPR at the end of this month. But regulators are usually slow to catch up. So for now it's going to have to be organizations that think about this. And think about, okay, when is it okay to treat different customers differently? Because if we, if we break that promise, customers are going to ultimately leave us. >> That's a hard problem. >> Right, right. >> You guys have a lot of these discussions internally? >> We do. >> And can you share those with us? >> Yeah, absolutely, we do. And we get a lot of questions. We often engage at the data strategy perspective. And it starts with, hey we've got great activity occurring in our business units, in our functional areas, but we don't really have a handle on the enterprise wide data strategy. And at that point we start talking about trust, and privacy, and security, and what is your what does your data flows look like. So it starts at that initial data strategy discussion. And one other thing I mentioned in my opening remarks this morning is, we released this blueprint and it's intended, as you said, to put a framework in process and reflect a lot of the lessons learned that we're all going through. I know you mentioned that many companies are looking at AI adoption, perhaps more so than we realized. And so the framework was intended to help accelerate that process. And then our big announcement today has been around the showcases, in particular our platform showcase. So it's really the platform we've built, within our organization. The components, the products, the capabilities that drives for us. And then with the intent of hopefully being, illustrative and helpful to clients that are looking to build similar capabilities. >> So let's talk about adoption. >> Brandon: Yeah, sure. >> Ya know, we... you often hear this bromide that we live in a world where, that pace of change is so fast. And things are changing so quickly it's hard to deny that. But then when you look at adoption of some of the big themes in our time. Whether it's big data or AI, digital, block chains, there are some major barriers to adoption. So you see them adopted in pockets. What's your perspective, and Forrester's perspective on adoption of, let's call it machine intelligence? >> Yeah, sure, so I mean, every year Forrester does a global survey of business and technology decision leaders called Business Technographics. And we ask folks about adoptions rates of certain technologies. And so when it comes to AI, globally, 52% of companies have adopted AI in some way. And another 20% plan to in the next 12 months. What's interesting to me, actually, is when you break that down geographically, the highest adoption rate, 60 plus percent, is in APAC, followed by North America, followed by Europe. And when you think about the privacy regulations in each of those geographies, well there are far fewer in APAC than there are, and will be, in Europe. And that's, I think kind of hamstringing adoption in that geography. Now is that a problem for Europe? I don't think so actually. I think AI, the way AI is going to be adopted in Europe is going to be more refined and respectful of customers' intrinsic right to privacy. >> Dave: Ya know I want... Go ahead. >> I've got to, I have to say Dave, I have to put a plug in. I've been a huge fan of Brandon's, for a long time. I've actually, ya know, a few years now of his research. And some of the research that you're mentioning, I hope people are reading it. Because we find these reports to be really helpful to understand, as you said, the specifics of adoptions, the trends. So I've got to put a plug in there. >> Thanks Caitlin. >> Because, the quality of the work and the insights are incredible. So that is why I was quite excited when Brandon accepted our offer to join us here in this session. >> Awesome. Yeah, so, let's dig into that a little bit. >> Brandon: Sure. >> So it seems like, so 52%, I'm wondering, what the other 48 are doing? They probably are, and they just don't know it. So it's possible that the study looks at, a strategy to adopt, presumably. I mean actively adopting. But it seems, I wonder if I could run this by you, get your comment. It seems that people will, organizations will more likely be buying AI as embedded in applications or systems or just kind of invisible. Then they won't necessarily be building it. I know many are trying to probably build it today. And what's your thought on that? In terms of just AI infused everywhere? >> So the first foray for most enterprises into this world of AI is chat bots for customer service. >> Dave: Sure. >> I mean we get a ton of inquires at Forrester about that. And there are a number of solutions. Ya know, IBM certainly has one for, that fulfill that need. And that's a very narrow use case, right? And it's also a value added of use case. If you can take more of those call center agents out of the loop, or at least accelerate or make them better at their jobs, then you're going to see efficiency gains. But this isn't this company wide AI transformation. It's just one very narrow use case. And usually that's, most elements of that are pre-built. We talked this morning, or the speakers this morning talked about commoditization of certain aspects of machine learning and AI. And it's very true. I mean, machine learning algorithms, many of them have been around for a long time, and you can access them for multiple different platforms. Even natural language processing, which a few years ago was highly inaccurate, is getting really, really accurate. So when, in a world where all of these things are commoditized, it's going to end up being how you implement them that's going to drive differentiation. And so, I don't think there's any problem with buying solutions that have been pre-built. You just have to be very thoughtful about how you use them to ultimately make decisions that impact the customer experience. >> I want to, in the time we have remaining, I want to get into the tech radar, the sort of taxonomy of AI or machine intelligence. You've done some work here. How do you describe, can you paint a picture, for what that taxonomy looks like? >> So I think most people watching realize AI is not one specific thing right? It's a bunch of components, technologies that stitched together lead to something that can emulate certain things that humans do, like sense the world around us, see, read, hear, that can think or reason. That's the machine learning piece. And that can then take action. And that's the kind of automation piece. And there are different core technologies that make up each of those faculties. The kind of emerging ones are deep learning. Of course you hear about it all the time. Deep learning is inherently the use of artificial neural networks, usually to take some unstructured data, let's say pictures of cats, and identify this is actually a cat right? >> Who would have thought? That we're led to this boom right? >> Right exactly. That was something you couldn't do five or six years ago, right? You couldn't actually analyze picture data like you analyze row and column data. So that's leading to a transformation. The problem there is that not a lot of people have this massive number of pictures of cats that are consistently and accurately labeled cat, not cat, cat, not cat. And that's what you need to make that viable. So a lot of vendors, and Watson has an API for this have already trained a deep neural network to do that so the enterprises aren't starting from scratch. And I think we'll see more and more of these kind of pre-trained solutions and companies gravitating towards the pre-trained solutions. And looking for differentiation, not in the solutions themselves, but again how they actually implement it to impact the customer experience. >> Hmmm, well that's interesting, just hearing you sense, see, read, hear, reason, act. These are words that describe not the past era. This is a new era that we're entering. We're in the cloud era now. We can sort of all agree with that. But these, the cloud doesn't do these things. We are clearly entering a new wave. Maybe it's driven by Watson's Law, or whatever holds out. Caitlin I'll give you the last word. Put a bumper sticker on this event, and where we're at here in 2018? >> I'll say, it's interesting to watch the themes evolve over the last few years. Ya know, we started with sort of a defensive posture. Most of our data executives were coming perhaps from an IT type background. We see a lot more with line of business, and chief operations type role. And we've seen the, we still king of the data warehouse, that's sort of how we described at the time. And now, I see our data leaders really driving transformation. They're responsible for both the data as well as the digital transformation. On the data side, it's the AI focus. And trying to really understand the deep learning capabilities, machine learning, that they're bringing to bear. So it's been, for me, it's been really interesting to see the topics evolve, see the role in the strategic piece of it. As well as see these guys elevated, in terms of influence within their organization. And then, our big topic this year was around AI and understanding it. And so, having Brandon to share his expertise was very exciting for me because, he's our lead analyst in the AI space. And that's what our attendees are telling us. They want to better understand, and better understand how to take action to implement and see those business results. So I think we're going to continue to see more of that. And yeah, it's been great to see, great to see it evolve. >> Well congratulations on taking the lead, this is a very important space. Ya know, a lot of people didn't really believe in it early on, thought the Chief Data Officer role would just sort of disappear. But you guys, I think, made the right investment and a good call, so congratulations on that. >> I was laughed out of the room when I proposed, I said hey we're hearing of this, doing a market scan of Chief Data Officer, either by title or something similar, titled responsible for enterprise wide data. I was laughed out of the room. I said let me do a qualitative piece. Let me interview 20 and just show, and then you're right, it was the thought was, role's going to go by the wayside. And I think we've seen the opposite. >> Oh yeah, absolutely. >> Data has grown in importance. The associative capabilities have grown. And I'm seeing these individuals, their scope, their sphere of responsibility really grow quite a bit. >> Yeah Forrester's tracked this. I mean, you guys I think just a few years ago was like eh, yeah 20% of organizations have a Chief Data Officer and now it's much much higher than that. >> Yeah, yeah, it's approaching 50%. >> Yeah, so, good. Alright Brandon, Caitlin, thanks very much for coming on theCUBE. >> Thanks for having us. >> Thank you, it was great. >> Keep it right there everybody. We'll be back, at the IBM Chief Data Officer Strategy Summit. You're watching theCUBE. (techno music) (telephone tones)