well you're watching the cube and I knew that you knew that I'm John Walls we're here in Las Vegas it's re invent 22. Big Show AWS putting it on the Big Show here late in 2022 that's going really well we're at the executive Summit right now sponsored by Accenture and we're going to talk about that relationship between Accenture and AWS um kind of where it is now and where it's going you know even bigger things down the road to help us do that two guests Andy Tay who's a senior managing director and the Accenture AWS business group lead at Accenture Andy thanks for being with us thanks for having me and Sarah whose last name was one of my all-time favorites all good because it is it's all good right okay it's all good Sarah all good worldwide leader of accenture's AWS business group for AWS and thank you both again for being here so let's talk about the relationship just in general high level here 30 000 feet a lot of great things have been happening we know a lot of great things are happening but how's this all you think evolved how did how has this come about that you two are just inextricably linked almost here in the cloud space Sarah why don't you jump on that yeah I'd love to um I think one of the the strongest factors that causes that Synergy for us is we both work backwards from our customer outcomes and so just by consistently doing that taking those customer signals um really obsessing over our customers success we know what we're marching towards and so then we kind of extract those themes and really work together to think about okay when we look at this holistically how do we go bigger better faster together and accomplish and solve those customer problems yeah Andy yeah John let me just maybe add and you know to amplify you know what Sarah just touched on um we both have common to our culture this notion of working from the client's perspective first so really delivering to the clients values or um you know in aws's parlance it's you know customer and so that's at the core and when we keep that at the core everything else becomes really easy where we invest what we build key clients we focus on what our team structure is et cetera Etc that's really easy so that sort of core core pillar number one in terms of our sort of you know success factors the second thing that I think really helps us is our sort of scale geographically you know certainly from an Accenture standpoint as you know John we're north of 800 000 people globally um couple that with aws's strength we really do have you know a field depth and breadth across the board that allows us to sort of see and feel what's happening in the market and allows us really to see around the corners as we like to think and say um and and that helps us be intentional on what we do um and then the third thing is really us we might know what we do but we sort of need to then play to our strengths and as you know we're two very different companies one focus on the technology side the other you know focus on the technology Services although we'll touch on you know some of the changes we're looking at as we go forward but that sort of playing to strength is key as well for us as a third pillar of success and so keeping those three things at the core really helps us move you know day to day and year by year and that's what you see in this continued partnership so what are you hearing from your customers these days we've talked a lot already today and it's kind of the buzzword you know modernization right everybody's talking about this transformation I don't care if you're in Mainframe or where you are everybody wants a modernized right now um you know what are you hearing from customers in that regard and I'm sure everybody's in a different state different yeah frame of mind you know some are embracing some are dragging uh what what's your take on the state of play right now well and I think it's like especially in these macroeconomic moments that we're in um time to value is critical for our customers um and then we have the talent shortage but even with those our customers still need us to solve for sustainability and still focus on inclusion diversity and equity and so we can't lower the bar in anything that we've already been doing we need to just keep doing more and building with them and so I think um for us really getting to the to the meat of what our customers need modernization is a big one but we're still seeing just so many of our customers look at basic transformation right how how do I dip in how do I start to move my environment move my people and get ready for what I need to do next for my business and so that that is a challenge and like we said with with the markets as volatile as they are right now I think a lot of customers are just trying to work with us to figure out how to do that in the most optimized and efficient way I just want to kind of rub people on the head and say it's going to be all right I mean it's so volatile as you pointed out Sarah right yeah I mean the market up and down and we're worried about a recession and companies and their plans they want to be Forward Thinking yeah but they've got to you know keep their powder dry too in some respects and get ready for that rainy day you know John it's funny um because you would think you know you've got the one hand you know rub that you know it's gonna be all right and and then on the other end you'll you know maybe clients should sort of hold temper and you know sort of just pause but I think clients get it they see it they feel it they understand the need to invest and I think you know there's a recent study back in 2008 those clients you know Sarah and I were reading the other day those clients who didn't invest ahead of those you know major if you remember those macroeconomic downturn times they came out really on the bad side um and so clients now are realizing that in these times these are the moments to invest and so they get it but they're faced with a couple of challenges one is time Sarah touched on you just don't have time and the second is Talent so we're working in a very intentional way on what we can do to help them there and and as you'll hear later on from Chris Wegman and Eric Farr um we're launching our velocity platform which really helps to compress that type and and get them faster you know time to Value we're also being very intentional on talent and how we help their talent so you know rotate so that we're not just taking the technology Journey but we're also having the people journey and then the third thing Sarah and I really focus on with our teams is figuring out new ways new sources of value for our clients and that's not just cost that's value the broader set and so we find that in moments like this it's actually an opportunity for us to really bring the best of AWS and Accenture to our clients well you hit value and I always find this one kind of tough because there is a big difference between cost and value my cost is X right whatever I write on my chat that's my cost so but but how do you help clients identify that value so that because it's you know it can be a little nebulous right can it not I mean it's uh but you have to validate you got to quantify at the end of the day because that's what the CEO wants to see it's what the CIO wants to see yeah you've got to identify values so how many how do you do that yeah yeah I mean we we have many different ways right velocity which Andy kind of touched on I think is is really um it's our foundational approach to help customers really kind of enter into their Cloud journey and focus on those key factors for Success right so we've got ISB Solutions built in there We've Got Talent and change built in we've got kind of what we're calling the fabric right that foundational technology layer and giving our customers all of that in a way that they can consume in a way that they can control and you know different modules essentially that they can leverage to move it's going to be tangible right they're going to be able to see I've now got access to all these things that I need I can move as I need to move and I'm not constantly you know looking around figuring out how to lock it all together we've given them that picture and that road map on how to really leverage this because we we need to be able to point to tangible outcomes and so that's critical yeah proof's got to be in the pudding and and you know to Sarah's point I think sort of we're entering into this sort of new dare I say new chapter of cloud and then you know sort of the first chapter was sort of those outcomes were around cost you know I've moved you into the cloud you can shut down your data center but now we've sort of got other sources of value now Beyond costs there's news new sources of revenue how do I become a platform company on top of the AWS cloud and then you know eke out new Revenue sources for myself how do I drive new experiences for my customers yeah um how do I maybe tap into the sustainability angle of things and how do I get greater Innovation from my talent how do I operate better in a Sarah said how do I become more Nimble more agile and more responsive to Market demands and so all those areas all those Dynamics all those outcomes are sources of value that were sort of really laser focused on and just ensuring that as a partnership we we help our clients on that Journey so what do you do about talent I mean you brought it up a couple of times UTP has um in terms of of training retaining recruiting all those key elements right now it's an ultra competitive environment right now yeah and there might be a little bit of a talent Gap in terms of what we're producing right so um you know how do you I guess make the most out of that and and make sure you keep the good people around yeah Talent is an interesting one John um and we were just touching on this uh before we got here um you know sort of from an Accenture standpoint um we're obviously focused on growing our AWS Talent um we've now got I think it's north of 27 000 people in Accenture with AWS certifications north of 34 000 certificates you know which is absolutely fantastic a small City it's just I mean it is very intentional in building that um as AWS rolls out new Services Adam touched on a whole bunch of them today we're at the core of that and ramping and building our talent so that we can drive and get our clients quicker to their value and then the second area of focus is what do we do to help our clients Talent how do we train them how do we enable them how do we you know get them to be more agile and you know being able to sort of operate in what we call that digital core operate in the cloud how do we do that and so we're focused um in in capabilities in fact our Accenture head of talent and people and change Christie Smith John is is here this week just for that and we're exploring ways in which we can get tighter and even more Innovative Around Talent and so I ultimately that that bleeds over to where the partnership goes right because if you can enhance that side of it then then everybody wins on that in terms of what you think you know where this is going yeah yeah it's already you know pretty good setup uh things are working pretty well but as the industry changes so rapidly and and you have to meet those needs how do you see the partnership evolving as well to meet those needs down the road we we have a very fortunate position in that our CEOs are both very engaged in this partnership and they push us think bigger go faster figure it out let's ride and there are definite pros and cons and some days I'm flying this close to the Sun but um it isn't a it's an absolute privilege to work with them the way that we get to and so we're always looking I mean Auntie said it earlier this is the relationship that helps us look around corners we've raised the bar and so we're constantly pushing each other pushing our teams just innovating together thinking it all through on where are we going and like I said reading those tea leaves reading those themes from our customers like hey we've just had five customers with the same similar feeling problem that we're trying to solve or we ran into the same issue in the field and how do we put that together and solve for it because we know it's not just five right we know they're more out there and so um I think you know it's it's leadership principles for us right at Amazon that guiding think big um you know insist on high standards that that'll always be core and Central to who we are and then you know fortunately Accenture has a really similar ethos yeah quick take on that Andy yeah I think as we look out you know I think um we're going to we've already seen but we're going to see this continued blurring of Industries um of um you know sort of clients moving into other Industries and yeah sort of this sort of agitation Market agitation um and so I think disruption you know disruption and and we're being you know focused on what do we need to be to do in order to help our clients on those Journeys and and to continue to you know get them you know faster Solutions is an area that we you know we are um really looking at and these are solutions that are either industry Solutions you'll hear a couple of them this week um you know we've got our insurance solution that we're we've developed as an intelligent underwriting capability leveraging AWS AIML to sort of be intelligent and cognitive um you know we've got other Solutions around the around Industries energy and Life Sciences but then also intelligent applications that might be touching you know areas I think earlier today Adam talked about AWS supply chain and that's an area that we are focused on and and proud to be a part of that and we're working very very closely with with Amazon on that uh to help you know our clients move ahead so I think we're going to see this continued blurring and we're going to obviously you know keep addressing that and just keep iterating well it looks like a relationship of trust and expertise right and it's worked out extremely well and uh if this is any indication where the interview went uh even better things are ahead for the partnership so thank you thank you for chiming in I appreciate your perspectives yeah thank you it's been great we continue our coverage here on thecube we're at re invent 22 we're in Las Vegas and you're watching thecube the leader in technical coverage foreign

>>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Okay. Welcome back to the cubes coverage of 80 us. Re-invent 2020 virtual ecentric executive summit. The two great guests here to break down the analysis of the relationship with cloud and essential. Brian bowhead director ahead of Accenture. 80 was a business group at Amazon web services. And Andy T a B G the M is essentially Amazon business group lead managing director at Accenture. Uh, I'm sure you're super busy and dealing with all the action, Brian. Great to see you. Thanks for coming on. So thank you. You guys essentially has been in the spotlight this week and all through the conference around this whole digital transformation, essentially as business group is celebrating its 50th anniversary. What's new, obviously the emphasis of next gen post COVID generation, highly accelerated digital transformation, a lot happening. You got your five-year anniversary, what's new. >>Yeah, it, you know, so if you look back it's exciting. Um, you know, so it was five years ago. Uh, it was actually October where we, where we launched the Accenture AWS business group. And if we think back five years, I think we're still at the point where a lot of customers were making that transition from, you know, should I move to cloud to how do I move to cloud? Right? And so that was one of the reasons why we launched the business group. And since, since then, certainly we've seen that transition, right? Our conversations today are very much around how do I move to cloud, help me move, help me figure out the business case and then pull together all the different pieces so I can move more quickly, uh, you know, with less risk and really achieve my business outcomes. And I would say, you know, one of the things too, that's, that's really changed over the five years. >>And what we're seeing now is when we started, right, we were focused on migration data and IOT as the big three pillars that we launched with. And those are still incredibly important to us, but just the breadth of capability and frankly, the, the, the breadth of need that we're seeing from customers. And obviously as AWS has matured over the years and launched our new capabilities, we're Eva with Accenture. Um, and in the business group, we've broadened our capabilities and deepened our capabilities over the, over the last five years as well. For instance, this year with, with COVID, especially, it's really forced our customers to think differently about their own customers or their citizens, and how do they serve as those citizens. So we've seen a huge acceleration around customer engagement, right? And we powered that with Accenture customer engagement platform powered by ADA, Amazon connect. And so that's been a really big trend this year. And then, you know, that broadens our capability from just a technical discussion to one where we're now really reaching out and, and, um, and helping transform and modernize that customer and citizen experience as well, which has been exciting to say, Andy want to get your thoughts here. We've >>Been reporting and covering essential for years. It's not like it's new to you guys. I mean, five years is a great anniversary. You know, check is good relationship, but you guys have been doing the work you've been on the trend line. And then this hits and Andy said on his keynote, and I thought he said it beautifully. And he even said it to me, my one-on-one interview with them was it's on full display right now, the whole digital transformation, everything about it is on full display and you're either were prepared for it or you kind of word, and you can see who's there. You guys have been prepared. This is not new. So give us the update from your perspective, how you're taking advantage of this, of this massive shift, highly accelerated digital transformation. >>Well, I think, I think you can be prepared, but you've also got to be prepared to always sort of, I think what we're seeing in, in, um, in, in, in, in recent times and particularly in two 20, what, what is it I think today there are, um, 4% of the enterprise workloads sits at the cloud. Um, you know, that leaves 96% out there on prem. Um, and I think over the next four to five years, um, we're going to see that sort of, uh, acceleration to the, to the cloud pick up, um, this year as Andy touched on, I think, uh, uh, on Tuesday in his, I think the pandemic is a forcing function, uh, for companies to, to, to really pause and think about everything from, from, you know, how they, um, manage that technology, their infrastructure, to, to clarity to where that data sets to what insights and intelligence that getting from that data. >>And then eventually even to, to the talent, the talent they have in the organization and how they can be competitive, um, that culture, that culture of innovation, of invention and reinvention. And so I think, I think, you know, when you, when you think of companies out there faced with these challenges, it forces us, it forces AWS's forces, AEG to come together and think through how can we help create value for them? How can we help help them move from sort of just causing and rethinking to having real plans in action and that taking them, uh, into, into implementation. And so that's, that's what we're working on. Um, I think over the next five years, we're looking to just continue to come together and, and help these companies get to the cloud and get the value from the cloud. Cause it's, it's beyond just getting to the cloud attached to me and living in the cloud and getting the value from it. >>It's interesting. Andy was saying, don't just put your toe in the water. You've got to go beyond the toe in the water kind of approach. Um, I want to get to that large scale cause that's the big pickup this week that I kind of walked away with was it's large scale. Acceleration's not just toe in the water experimentation. Can you guys share, what's causing this large scale end to end enterprise transformation and what are some of the success criteria have you seen for the folks who have done that? Yeah. And I'll, I'll start in the end. >>You can buy a lawn. So you, it's interesting if I look >>Back a year ago at reinvent and when I did the cube interview, then we were talking about how ABG we're >>Starting to see that shift of customers. You know, we've been working with customers for years on a single of what I call a single-threaded programs, right? We can do a migration, we can do SAP, we can do a data program. And then even last year we were really starting to see customers ask. The question is like, what kind of synergies and what kind of economies of scale do I get when I start bringing these different threads together and also realizing that it's, you know, to innovate for the business and build new applications, new capabilities, well, that, that is going to inform what data you need to, to hydrate those applications, right? Which then informs your data strategy while a lot of that data is then also embedded in your underlying applications that sit on premises. So you should be thinking through how do you get those applications into the cloud? >>So you need to draw that line through all of those layers. And that was already starting last year. And so last year we launched the joint transformation program with AEG. And then, so we were ready when this year happened and then it was just an acceleration. So things have been happening faster than we anticipated, but we knew this was going to be happening. And luckily we've been in a really good position to help some of our customers really think through all those different layers of kind of the pyramid as we've been calling it along with the talent and change pieces, which are also so important as you make this transformation to cloud >>Andy, what's the success factors. Andy Jassy came on stage during the partner day, a surprise fireside chat with Doug Hume and talking about this is really an opportunity for partners to, to change the business landscape with enablement from Amazon. You guys are in a pole position to do that in the marketplace. What's the success factors that you see, >>Um, really from three, three fronts, I'd say, um, w you know, one is the, the people. Um, and, and I, I, again, I think Andy touched on sort of a, uh, success factors, uh, early in the week. And for me, it's these three areas that it sort of boils down to, to these three areas. Um, one is the, the, the, the people, uh, from the leaders that it's really important to set those big, bold visions point the way. And then, and then, you know, set top down goals. How are we going to measure you almost do get what you measure, um, to be, you know, beyond the leaders, to, to the right people in the right position across the company. We're finding a key success factor for these end to end transformations is not just the leaders, but you haven't poached across the company, working in a, in a collaborative, shared, shared success model, um, and people who are not afraid to, to invent and fail. >>And so that takes me to perhaps the second point, which is the culture. Um, it's important, uh, with finding food for the right conditions to be set in the company, not enable people to move at pace, move at speed, be able to fail fast, um, keep things very, very simple, and just keep iterating and that sort of culture of iteration, um, and improvement versus seeking perfection is, is super important for, for success. And then the third part of maybe touch on is, is partners. Um, I think, you know, as we move forward over the next five years, we're going to see an increasing number of players in the ecosystem in the enterprises state. Um, you're going to see more and more SAS providers. And so it's important for companies and our joint clients out there to pick partners like, um, like AWS or, or Accenture or others, but to pick partners who have all worked together and built solutions together. And that allows them to get speed to value quicker. It allows them to bring in pre-assembled solutions, um, and really just drive that transformation in a quicker, it sorts of manner. >>Yeah, that's a great point worth calling out, having that partnership model that's additive and has synergy in the cloud, because one of the things that came out of this this week, this year is reinvented, is there's new things going on in the public cloud, even though hybrid is an operating model, outpost and super relevant. There, there are benefits for being in the cloud and you've got partners, APIs, for instance, and have microservices working together. This is all new, but I got, I got to ask that on that thread, Andy, where did you see your customers going? Because I think, you know, as you work backwards from the customers, you guys do, what's their needs, how do you see them? You know, where's the puck going? Where can they skate to where the puck's going? Because you can almost look forward and say, okay, I've got to build modern apps. I got to do the digital transformation. Everything is a service. I get that, but what do they, what, what solutions are you building for them right now to get there? >>Yeah. And, and of course, with, with, you know, industries blurring and multiple companies, it's always hard to boil down to the exact situations, but you can probably look at it from a sort of a thematic lens. And what we're seeing is as the cloud transformation journey picks up from us perspective, we've seen a material shift in the solutions and problems that we're trying to address with clients that they are asking for us, uh, to, to help, uh, address is no longer just the back office where you're sort of looking at cost and efficiency and, um, uh, driving gains from that perspective. It's beyond that, it's now materially the top line. It's, how'd you get the driving to the, you know, speed to insights, how'd you get them decomposing, uh, their application set in order to derive those insights. Um, how'd you get them, um, to, to, um, uh, sort of adopt leading edge industry solutions that give them that jump start, uh, and that accelerant to winning the customers, winning the eyeballs. >>Um, and then, and then how'd you help drive the customer experience. We're seeing a lot of push from clients, um, or ask for help on how do I optimize my customer experience in order to retain my eyeballs. And then how do I make sure I've got a soft self-learning ecosystem at play, um, where I, you know, it's not just a practical experience, but I can sort of keep learning and iterating, um, how treat my, eat, my customers, um, and a lot of that, um, that's still self-learning that comes from, you know, putting in, uh, intelligence into your, into your systems, getting an AI and ML, uh, in that. And so as a result of that, where it was seeing a lot of push and a lot of what we're doing, uh, is pouring investments into those areas. And then finally, maybe beyond the bottom line and the top line is how do you harden that and protect that with, um, security and resilience? Uh, so I'll probably say those are the three areas. John >>Brian on the business model side, obviously the enablement is what Amazon has. Um, we see things like SAS factory coming on board and the partner network I've see a, is a big, huge partner of you guys. Um, the business models there. You've got I, as, as doing great with chips, you have this data modeling this data opportunity to enable these modern apps. We heard about the partner strategy from Andy. I'm talking about yesterday now about how can partners within even a center. What's the business model side on your side that you're enabling this. Can you just share your thoughts on that? >>Yeah. And so it's, it's interesting. And again, I'm kind of build it in a build a little bit on some of the things that Andy really talked about there, right? And that we, if you think of that from the partnership, we are absolutely helping our customers with kind of that it modernization piece and we're investing a lot and that there's hard work that needs to get done there. And we're investing a lot as a partnership around the tools, the assets and the methodology. So in AWS and Accenture show up together as AEG, we are executing off a single blueprint with a single set of assets so we can move fast. So we're going to continue to do that with all the hybrid announcements from this past week, those get baked into that, that migration modernization theme, but the other really important piece here as we go up the stack, Andy mentioned it, right? >>The data piece, like so much of what we're talking about here is around data and insights. Right? I did a cube interview last week with, uh, Carl hick. Um, who's the CIO from Takeda. And if you hear Christophe Weber from Takeda talk, he talks about Takeda being a data company, data and insights company. So how do we, as a partnership, again, build the capabilities and the platforms like with Accenture's applied insights platform so that we can bootstrap and really accelerate our client's journey. And then finally, on the innovation on the business front, and Andy was touching on some of these, we are investing in industry solutions and accelerators, right? Because we know that at the end of the day, a lot of these are very similar. We're talking about ingesting data, using machine learning to provide insights and then taking action. So for instance, the cognitive insurance platform that we're working together on with Accenture, if they get about property and casualty claims and think about how do we enable touchless claims using machine learning and computer vision that can assess based on an image damage, and then be able to triage that and process it accordingly, right? >>Using all the latest machine learning capabilities from AWS >>With that deep, um, AI machine learning data science capability from Accenture, who knows all those algorithms that need to get built and build that library by doing that, we can really help these insurance companies accelerate their transformation around how they think about claims and how they can speed those claims on behalf of their policy holder. So that's, what's an example of a, kind of like a bottom to top view of what we're doing in the partnership to address these new needs. >>That's awesome. Andy, I want to get back to your point about culture. You mentioned it twice now. Um, challenge is a big part of the game here. Andy Jassy referenced Lambda. Next generation developers were using Lambda. He talked about CIO stories around, they didn't move fast enough. They lost three years. A new person came in and made it go faster. This is a new, this is a time for a certain kind of, um, uh, professional and individual, um, to, to be part of, um, this next generation. What's the talent strategy you guys have to attract and attain the best and retain the best people. How do you do it? >>Um, you know, it's, it's, um, it's an interesting one. It's, it's, it's oftentimes a, it's, it's a significant point and often overlooked. Um, you know, people, people really matter and getting the right people, um, in not just in AWS or, but then on our customers is super important. We often find that much of our discussions with, with our clients is centered around that. And it's really a key ingredient. As you touched on, you need people who are willing to embrace change, but also people who are willing to create new, um, to invent new, to reinvent, um, and to keep it very simple. Um, w we're we're we're seeing increasingly that you need people who have a sort of deep learning and a deep, uh, or deep desire to keep learning and to be very curious as, as they go along. Most of all, though, I find that, um, having people who are not willing or not afraid to fail is critical, absolutely critical. Um, and I think that that's, that's, uh, a necessary ingredient that we're seeing, um, our clients needing more off, um, because if you can't start and, and, and you can't iterate, um, you know, for fear of failure, you're in trouble. And I think Andy touched on that you, you know, where that CIO, that you referred to last three years, um, and so you really do need people who are willing to start not afraid to start, um, and, uh, and not afraid to lead. Yeah. >>It takes a gut-check there. I just said, you guys have a great team over there. Everyone at the center I've interviewed strong, talented, and not afraid to lean in and, and into the trends. Um, I got to ask on that front cloud first was something that was a big strategic focus for Accenture. How does that fit into your business group? That's, uh, Amazon focus, obviously their cloud, and now hybrid everywhere, as I say, um, how does that all work it out? >>We're super excited about our cloud first initiative, and I think it fits it, um, really, uh, perfectly it's it's, it's what we needed. It's, it's, it's a, it's another accelerant. Um, if you think of first, what we're doing is we're, we're putting together, um, a capability set that will help enable him to and translations as Brian touched on your help companies move, you know, from just, you know, migrating to, to, to modernizing, to driving insights, to bringing in change, um, and, and, and helping on that, on that talent. So that's sort of component number one is how does Accenture bring the best, uh, end to end transformation capabilities to our clients? Number two is perhaps, you know, how do we, um, uh, bring together pre-assembled as Brian touched on preassembled industry offerings to help as an accelerant, uh, for our, for our customers three, as, as we touched on earlier, is, is that sort of partnership with the ecosystem. >>We're going to see an increasing number of SAS providers in an estate in the enterprises States out there. And so, you know, parts of our cloud first and our AEG strategy is to increase our touchpoints and our integrations and our solutions and our offerings where the ecosystem partners out there, the ISV partners out there, and the SAS providers out there. And then number four is really about, you know, how do we, um, extend the definition of the cloud? I think oftentimes people thought of the cloud just as sort of on-prem and prem. Um, but, but as Andy touched on earlier this week, you know, you've, you've got this, the concept of hybrid cloud and that in itself, um, uh, is, is, is, uh, you know, being redefined as well, you know, where you've got the intelligent edge and you've got various forms of the edge. Um, so that's the fourth part of, of our, of our cloud first strategy. And, and, and for us was super excited because all of that is highly relevant for ABG, as we look to build those capabilities as industry solutions and others, and as we look to enable our customers, but also how we, you know, as we, as we look to extend how we go to market, uh, I joined tally PS, uh, in, uh, in our respective skews and products. >>Well, what's clear now is that people now realize that if you contain that complexity, the upside is massive. And that's great opportunity for you guys. We got to get to the final question for you guys to weigh in on, as we wrap up next five years, Brian, Andy weigh in, how do you see that playing out? What do you see this exciting, um, for the partnership and the cloud first cloud, everywhere cloud opportunities share some perspective. >>Yeah, I, I, they, you know, just kinda building on that cloud first, right? What cloud first. And we were super excited when cloud first was announced and you know, what it signals to the market and what we're seeing in our customers, which is cloud really permeates everything that we're doing now. Um, and, and so all aspects of the business will get infused with cloud in some ways, you know, it, it touches on all pieces. And I think what we're going to see is just a continued acceleration and getting much more efficient about pulling together the disparate, what had been disparate pieces of these transformations, and then using automation using machine learning to go faster. Right? And so, as we start thinking about the stack, right, well, we're going to get, I know we are, as a partnership is we're already investing there and getting better and more efficient every day as the migration pieces and the moving assets, the cloud are just going to continue to get more automated, more efficient, and those will become the economic engines that allow us to fund the differentiated, innovative app activities up the stack. >>So I'm excited to see us, you know, kind of invest to make those, those, um, those bits accelerated for customers so that we can free up capital and resources to invest where it's going to drive the most outcome for their end customers. Um, and I think that's going to be a big focus and that's going to have the industry, um, you know, focus. It's going to be making sure that we can consume the latest and greatest of AWS has capabilities and, you know, in the areas of machine learning and analytics, but then Andy's also touched on it bringing in ecosystem partners, right? I mean, one of the most exciting wins we had this year, and this year of COVID is looking at the universe, uh, looking at Massachusetts, the COVID track and trace solution that we put in place is a partnership between Accenture, AWS, and Salesforce, right? So again, bringing together three really leading partners who can deliver value for our customers. I think we're going to see a lot more of that. As customers look to partnerships like this, to help them figure out how to bring together the best of the ecosystem to drive solutions. So I think we're going to see more of that as well. >>All right, Andy final word, your take >>Of innovation is, is picking up. Um, the split things are just going faster and faster. I'm just super excited and looking forward to the next five years as, as you know, the technology invention, um, comes out and continues to sort of set new standards from AWS. Um, and as we, as Accenture bringing our industry capabilities, we marry the two, we, we go and help our customers super exciting times. >>Well, congratulations on the partnership. I want to say thank you to you guys, because I've reported a few times some stories around real successes around this COVID pandemic that you guys worked together on with Amazon that really changed people's lives. Uh, so congratulations on that too as well. I want to call that out. Thanks for coming >>Up. Thank you. Thanks for coming on. >>Okay. This is the cubes coverage, Accenture AWS partnership, part of the center's executive summit at Avis reinvent 2020. I'm John for your host. Thanks for watching.

>> Announcer: From around the globe, it's theCUBE. With digital coverage of AWS reInvent Executive Summit 2020, sponsored by Accenture and AWS. >> Hello and welcome back to theCUBE's coverage of AWS reInvent 2020. This is special programming for the Accenture Executive Summit where all the thought leaders are going to extract the signal from those share with you their perspective of this year's reInvent conference as it respects the customers' digital transformation. Brian Bohan is the director and head of Accenture, AWS Business Group at Amazon web services. Brian, great to see you. And Chris Wegmann is the Accenture Amazon Business Group technology lead at Accenture. Guys this is about technology vision this conversation. Chris, I want to start with you because you're Andy Jackson's keynote. You heard about the strategy of digital transformation, how you got to lean into it. You got to have the guts to go for it and you got to decompose. He went everywhere.(chuckles) So what did you hear? What was striking about the keynote? Because he covered a lot of topics. >> Yeah. It was epic as always from Andy. Lot of topics, a lot to cover in the three hours. There was a couple of things that stood out for me. First of all, hybrid. The concept, the new concept of hybrid and how Andy talked about it, bringing the compute and the power to all parts of an enterprise, whether it be at the edge or are in the big public cloud, whether it be in an Outpost or wherever it'd be, right with containerization now. Being able to do Amazon containerization in my data center and that's awesome. I think that's going to make a big difference. All that being underneath the Amazon console and billing and things like that, which is great. I'll also say the chips, right? I know computer is always something that we always kind of take for granted but I think again, this year, Amazon and Andy really focused on what they're doing with the chips and compute and the compute is still at the heart of everything in cloud. And that continued advancement is making an impact and will make and continue to make a big impact. >> Yeah, I would agree. I think one of the things that really... I mean the container thing was I think really kind of a nuance point. When you've got Deepak Singh on the opening day with Andy Jassy and he runs a container group over there. When we need a small little team, he's on the front stage. That really is the key to the hybrid. I think this showcases this new layer. We're taking advantage of the Graviton2 chips, which I thought was huge. Brian, this is really a key part of the platform change, not change, but the continuation of AWS. Higher level servers, >> Yep. building blocks that provide more capabilities, heavy lifting as they say but the new services that are coming on top really speaks to hybrid and speaks to the edge. >> It does. Yeah. I think like Andy talks about and we talked about we really want to provide choice to our customers, first and foremost. And you can see that in the array of services we have, we can see it in the the hybrid options that Chris talked about. Being able to run your containers through ECS or EKS anywhere. It just get to the customers choice. And one of the things that I'm excited about as you talk about going up the stack and on the edge are things, most certainly Outpost, right? So now Outpost was launched last year but then with the new form factors and then you look at services like Panorama, right? Being able to take computer vision and embed machine learning and computer vision, and do that as a managed capability at the edge for customers. And so we see this across a number of industries. And so what we're really thinking about is customers no longer have to make trade-offs and have to think about those choices, that they can really deploy natively in the cloud and then they can take those capabilities, train those models, and then deploy them where they need to whether that's on premises or at the edge, whether it be in a factory or retail environment. I think we're really well positioned when hopefully next year we start seeing the travel industry rebound and the need more than ever really to kind of rethink about how we kind of monitor and make those environments safe. Having this kind of capability at the edge is really going to help our customers as we come out of this year and hopefully rebound next year. >> Chris, I want to go back to you for a second. It's hard to pick your favorite innovation from the keynote because, Brian, just reminded me of some things I forgot happened. It was like a buffet of innovation. Some keynotes have one or two, there was like 20. You got the industrial piece that was huge. Computer vision, machine learning, that's just a game changer. The connect thing came out of nowhere in my opinion. I mean, it's a call center technology so it's boring as hell, what are you going to do with that?(Brian and Chris chuckle) It turns out it's a game changer. It's not about the calls but the contact and that's distant intermediating in the stack as well. So again, a feature that looks old is actually new and relevant. What was your favorite innovation announcement? >> It's hard to say. I will say my personal favorite was the Mac OS. I think that is a phenomenal just addition, right? And the fact that AWS has worked with Apple to integrate the Nitro chip into the iMac and offer that out. A lot of people are doing development for IOS and that stuff and that's just been a huge benefit for the development teams. But I will say, I'll come back to Connect. You mentioned it but you're right. It's a boring area but it's an area that we've seen huge success with since Connect was launched and the additional features that Amazon continues to bring, obviously with the pandemic and now that customer engagement through the phone, through omni-channel has just been critical for companies, right? And to be able to have those agents at home, working from home versus being in the office, it was a huge advantage for several customers that are using Connect. We did some great stuff with some different customers but the continue technology like you said, the call translation and during a call to be able to pop up those keywords and have a supervisor listen is awesome. And some of that was already being done but we are stitching multiple services together. Now that's right out of the box. And that Google's location is only going to make that go faster and make us to be able to innovate faster for that piece of the business. >> It's interesting not to get all nerdy and business school like but you've got systems of records, systems of engagement. If you look at the call center and the Connect thing, what got my attention was not only the model of disintermediating that part of the engagement in the stack but what actually cloud does to something that's a feature or something that could be an element like say call center, the old days of calling the 800 number and getting some support. You got infra chip, you have machine learning, you actually have stuff in the in the stack that actually makes that different now. The thing that impressed me was Andy was saying, you could have machine learning detect pauses, voice inflections. So now you have technology making that more relevant and better and different. So a lot going on. This is just one example of many things that are happening from a disruption innovation standpoint. What do you guys think about that? Am I getting it right? Can you share other examples? >> I think you are right and I think what's implied there and what you're saying and even in the other Mac OS example is the ability... We're talking about features, right? Which by themselves you're saying, Oh, wow! What's so unique about that? But because it's on AWS and now because whether you're a developer working with Mac iOS and you have access to the 175 plus services that you can then weave into your new application. Talk about the Connect scenario. Now we're embedding that kind of inference and machine learning to do what you say, but then your data Lake is also most likely running in AWS, right? And then the other channels whether they be mobile channels or web channels or in-store physical channels, that data can be captured and that same machine learning could be applied there to get that full picture across the spectrum, right? So that's the power of bringing you together on AWS, the access to all those different capabilities and services and then also where the data is and pulling all that together for that end to end view. >> Can you guys give some examples of work you've done together? I know there's stuff we've reported on, in the last session we talked about some of the connect stuff but that kind of encapsulates where this is all going with respect to the tech. >> Yeah. I think one of them, it was called out on Doug's Partner Summit is a SAP Data Lake Accelerator, right? Almost every enterprise has SAP, right? And getting data out of SAP has always been a challenge, right? Whether it be through data warehouses and AWS, or sorry, SAP BW. What we've focused on is getting that data when you have SAP on AWS, getting that data into the Data Lake, right? Getting it into a model that you can pull the value out and the customers can pull the value out, use those AI models. So that's one thing we worked on in the last 12 months. Super excited about seeing great success with customers. A lot of customers had ideas. They want to do this, they had different models. What we've done is made it very simplified. Framework which allows customers to do it very quickly, get the data out there and start getting value out of it and iterating on that data. We saw customers are spending way too much time trying to stitch it all together and trying to get it to work technically. And we've now cut all of that out and they can immediately start getting down to the data and taking advantage of those different services that are out there by AWS. >> Brian, you want to weigh in as things you see as relevant builds that you guys done together that kind of tease out the future and connect the dots to what's coming? >> I'm going to use a customer example. We worked with, it just came out, with Unilever around their blue air, connected, smart air purifier. And what I think is interesting about that, I think it touches on some of the themes we're talking about as well as some of the themes we talked about in the last session, which is we started that program before the pandemic, but Unilever recognized that they needed to differentiate their product in the marketplace, move to more of a services oriented business which we're seeing as a trend. We enabled this capability. So now it's a smart air purifier that can be remote managed. And now when the pandemic hit, they are in a really good position, obviously, with a very relevant product and capability to be used. And so, that data then as we were talking about is going to reside on the cloud. And so the learning that can now happen about usage and about filter changes, et cetera can find its way back into future iterations of that picked out that product. And I think that's keeping with what Chris is talking about where we might be systems of record like in SAP, how do we bring those in and then start learning from that data so that we can get better on our future iterations? >> Hey, Chris, on the last segment we did on the business mission session, Andy Tay from your team talked about partnerships within a century and working with other folks. I want to take that now on the technical side because one of the things that we heard from Doug's keynote and during the partner day was integrations and data were two big themes. When you're in the cloud technically, the integrations are different. You're going to get unique things in the public cloud that you're just not going to get on-premise access to other cloud native technologies and companies. How do you see the partnering of Accenture with people within your ecosystem and how the data and the integration play together? What's your vision? >> Yeah. I think there's two parts of it. One there's from a commercial standpoint, right? Some marketplace, you heard Dave talk about that in the partner summit, right? That marketplace is now bringing together this ecosystem in a very easy way to consume by the customers and by the users and bringing multiple partners together. And we're working with our ecosystem to put more products out in the marketplace that are integrated together already. I think one from a technical perspective though. If you look at Salesforce, I talked a little earlier about Connect. Another good example technically underneath the covers, how we've integrated Connect and Salesforce, some of it being pre-built by AWS and Salesforce, other things that we've added on top of it, I think are good examples. And I think as these ecosystems these ISVs put their products out there and start exposing more and more APIs on the Amazon platform may opening it up, having those pre-built network connections there between the different VPCs of the different areas within within a customer's network and having them all opened up and connected and having all that networking done underneath the covers. It's one thing to call the APIs, it's one thing to have access to those and that's not a big focus of a lot of ISVs and customers who build those APIs and expose them but having that network infrastructure underneath and being able to stay within the cloud, within AWS to make those connections that pass that data. We always talk about scale, right? It's one thing if I just need to pass like a simple user ID back and forth, right? That's fine. We're not talking massive data sets, whether it be seismic data or whatever it be, passing those large data sets between customers across the Amazon network is going to open up the world. >> Yeah, I see huge possibilities there and love to keep on this story. I think it's going to be important and something to keep track of. I'm sure you guys will be on top of it. One of the things I want to dig into with you guys now is Andy had kind of this philosophical thing in his keynote talk about societal change and how tough the pandemic is. Everything's on full display and this kind of brings out kind of like where we are and the truth. If you look at the truth it's a virtual event. I mean, it's a website and you got some sessions out there, we're doing remote best we can and you've got software and you've got technology and the other concept of a mechanism, it's software, it does something It does a purpose. Accenture, you guys have a concept called Living Systems where growth strategy powered by technology. How do you take the concept of a living organism or a system and replace the mechanism staleness of computing and software? And this is kind of interesting because we're on the cusp of a major inflection point post COVID. I get the digital transformation being slow. That's yes, that's happening. There's other things going on in society. What do you guys think about this Living Systems concept? Yeah. I'll start. I think the living system concept, it started out very much thinking about how do you rapidly change your system, right? And because of cloud, because of DevOps, because of all these software technologies and processes that we've created, that's where it started making it much easier, make it a much faster being able to change rapidly. But you're right. I think if you now bring in more technologies, the AI technology, self-healing technologies. Again, you heard Andy in his keynote talk about the systems and services they're building to detect problems and resolve those problems, right? Obviously automation is a big part of that. Living Systems, being able to bring that all together and to be able to react in real time to either when a customer asks, either through the AI models that have been generated and turning those AI models around much faster and being able to get all the information that came in the last 20 minutes, right? Society is moving fast and changing fast and even in one part of the world, if something in 10 minutes can change. And being able to have systems to react to that, learn from that and be able to pass that on to the next country especially in this world of COVID and things changing very quickly and diagnosis and medical response all that so quickly to be able to react to that and have systems pass that information, learn from that information is going to be critical. >> That's awesome. Brian, one of the things that comes up every year is, oh, the cloud's scalable. This year I think we've talked on theCUBE before, years ago certainly with the Accenture and Amazon. I think it was like three or four years ago. Yeah. The clouds horizontally scalable but vertically specialized at the application layer. But if you look at the Data Lake stuff that you guys have been doing where you have machine learning, the data is horizontally scalable and then you got the specialization in the app changes the whole vertical thing. You don't need to have a whole vertical solution or do you? So, how has this year's cloud news impacted vertical industries? Because it used to be, oh, oil and gas, financial services. They've got a team for that. We got a stack for that. Not anymore. Is it going away? What's changing? >> Well. It's a really good question. I think what we're seeing, and I was just on a call this morning talking about banking and capital markets and I do think the challenges are still pretty sector specific. But what we do see is the kind of commonality when we start looking at the, and we talked about this, the industry solutions that we're building as a partnership, most of them follow the pattern of ingesting data, analyzing that data and then being able to provide insights and then actions, right? So if you think about creating that kind of common chassis of that in just the Data Lake and then the machine learning, and you talk about the nuances around SageMaker and being able to manage these models, what changes then really are the very specific industries' algorithms that you're writing, right, within that framework. And so, we're doing a lot and Connect is a good example of this too, where you look at it and yeah, customer service is a horizontal capability that we're building out, but then when you stamp it into insurance or retail banking, or utilities, there are nuances then that we then extend and build so that we meet the unique needs of those industries and that's usually around those models. >> Yeah. I think this year was the first reInvent that I saw real products coming out that actually solved that problem. I mean, it was there last year SageMaker was kind of moving up the stack, but now you have apps embedding machine learning directly in and users don't even know it's in there. I mean, cause this is kind of where it's going, right? I mean-- >> You saw that was in announcements, right? How many announcements where machine learning is just embedded in? I mean, CodeGuru, DevOps Guru, the Panorama we talked about, it's just there. >> Yeah. I mean having that knowledge about the linguistics and the metadata, knowing the business logic, those are important specific use cases for the vertical and you can get to it faster. Chris, how is this changing on the tech side, your perspective? >> Yeah. I keep coming back to AWS and cloud makes it easier, right? All this stuff can be done and some of it has been done, but what Amazon continues to do is make it easier to consume by the developer, by the customer and to actually embed it into applications much easier than it would be if I had to go set up the stack and build it all on them and embed it, right? So it's shortcoming that process and again, as these products continue to mature, right, and some of this stuff is embedded, it makes that process so much faster. It reduces the amount of work required by the developers the engineers to get there. So, I'm expecting you're going to see more of this, right. I think you're going to see more and more of these multi connected services by AWS, that has a lot of the AI ML pre-configured Data Lakes, all that kind of stuff embedded in those services. So you don't have to do it yourself and continue to go up the stack. And we always talk about Amazon's built for builders, right? But, builders have been super specialized and are becoming, as engineers were being asked to be bigger and bigger and to be be able to do more stuff and I think these kind of integrated services are going to help us do that >> And certainly needed more now when you have hybrid edge that they're going to be operating with microservices on a cloud model and with all those advantages that are going to come around the corner for being in the cloud. I mean, I think there's going to be a whole clarity around benefits in the cloud with all these capabilities and benefits. Cloud Guru I think it's my favorite this year because it just points to why that could happen. I mean that happens because of the cloud data.(laughs) If you're on-premise, you may not have a little Cloud Guru. you are going to get more data but they're all different. Edge certainly will come in too. Your vision on the edge, Chris, how you see that evolving for customers because that could be complex, new stuff. How is it going to get easier? >> Yeah. It's super complex now, right? I mean, you got to design for all the different edge 5G protocols are out there and solutions, right? Amazon's simplifying that. Again, I come back to simplification, right? I can build an app that works on any 5G network that's been integrated with AWS, right. I don't have to set up all the different layers to get back to my cloud or back to my my bigger data set. And that's kind of choking. I don't even know where to call the cloud anymore. I got big cloud which is a central and I go down then you've got a cloud at the edge. Right? So what do I call that? >> Brian: It's just really computing.(laughing) Exactly. So, again, I think is this next generation of technology with the edge comes right and we put more and more data at the edge. We're asking for more and more compute at the edge, right? Whether it be industrial or for personal use or consumer use, that processing is going to get more and more intense to be able to maintain under a single console, under a single platform and be able to move the code that I developed across that entire platform, whether I have to go all the way down to the very edge at the 5G level, right, or all the way back into the bigger cloud and how that processing in there, being able to do that seamlessly is going to allow the speed of development that's needed. >> Wow. You guys done a great job and no better time to be a techie or interested in technology or computer science or social science for that matter. This is a really perfect store. A lot of problems to solve, a lot of change happening, positive change opportunities, a lot of great stuff. Final question guys. Five years working together now on this partnership with AWS and Accenture. Congratulations, you guys are in pole position for the next wave coming. What's exciting you guys? Chris, what's on your mind? Brian, what's getting you guys pumped up? >> Well, again, I come back to Andy mentioned it in his keynote, right? We're seeing customers move now, right. Five years ago we knew customers were going to do this. We built a partnership to enable these enterprise customers to make that journey, right? But now, even more we're seeing them move at such great speed, right? Which is super excites me, right? Because I can see... Being in this for a long time now, I can see the value on the other end. We've been wanting to push our customers as fast as they can through the journey and now they're moving. Now they're getting the religion, they're getting there. They see they need to do it to change your business so that's what excites me. It just the excites me, it's just the speed at which we're going to to see the movement. >> Yeah. >> Yeah. I'd agree with that. I mean, I just think getting customers to the cloud is super important work and we're obviously doing that and helping accelerate that. It's what we've been talking about when we're there all the possibilities that become available, right? Through the common data capabilities, the access to the 175 somewhat AWS services. I also think and this is kind of permeated through this week at Re:invent is the opportunity, especially in those industries that do have an industrial aspect, a manufacturing aspect, or a really strong physical aspect of bringing together IT and operational technology and the business with all these capabilities and I think edge and pushing machine learning down to the edge and analytics at the edge is really going to help us do that. And so I'm super excited by all that possibility because I feel like we're just scratching the surface there. >> It's a great time to be building out. and this is the time for reconstruction, reinvention. Big theme, so many storylines in the keynote and the events . It's going to keep us busy here at SiliconANGLE on theCUBE for the next year. Gentlemen, thank you for coming on. I really appreciate it. Thanks. >> Thank you. All right. Great conversation. We're getting technical. We're going to go another 30 minutes A lot to talk about. A lot of storylines here at AWS Re:Invent 2020 at the Accenture Executive Summit. I'm John Furrier. Thanks for watching. (upbeat music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation as we've been reporting the Koba 19 pandemic has created a bifurcated IT spending picture and over the last several weeks we've reported both on the macro and even some come at it from from a vendor and a sector view I mean for example we've reported on some of the companies that have really continued to thrive we look at the NASDAQ and its you know near at all-time highs companies like oh and in CrowdStrike we've reported on snowflake uipath the sectors are PA some of the analytic databases around AI maybe even to a lesser extent cloud but still has a lot of tailwind relative to some of those on-prem infrastructure plays even companies like Cisco bifurcated in and of themselves where you see this Meraki side of the house you know doing quite well the work from home stuff but maybe some of the traditional networking not as much well now what if you flip that to really try to understand what's going on with the shape of the recovery which is the main narrative right now is it a v-shape does it a u-shape what is what's that what do people expect and now you understand that you really have to look at different industries because different industries are going to come back at a different pace with me again is Sagar khadiyah who's the director of research at EGR Sagar you guys are all over this as usual timely information it's great to see you again hope all is well in New York City thanks so much David it's a pleasure to be back on again yeah so where are we in the cycle we give dividend a great job and very timely ETR was the first to really put out data on the koban impact with the survey that ran from mid-march to to mid-april and now everybody's attention sagar is focused on okay we're starting to come back stores are starting to open people are beginning to to go out again and everybody wants to know what the shape of the recovery looks like so where are we actually in that research cycle for you guys yeah no problem so like you said you know in that kind of march/april timeframe we really want to go out there and get an idea of what we're doing the budget impacts you know as it relates to IT because of kovat 19 right so we kind of ended off there around a decline of 5% and coming into the year the consensus was of growth of 4 or 5% right so we saw about a 900,000 basis points wing you know to the negative side and the public covered in March and April were you know which sectors and vendors were going to benefit as a result of work from home and so now as we kind of fast forward to the research cycle as we kind of go more into May and into the summer rather than asking those exact same question to get again because it's just been you know maybe 40 or 50 days we really want Singh on the recovery type as well as kind of more emerging private vendors right we want to understand what's gonna be the impact on on these vendors that typically rely on you know larger conferences more in-person meetings because these are younger technologies there's not a lot of information about them and so last Thursday we launched our biannual emerging technology study it covers roughly 300 private emerging technologies across maybe 60 sectors of technology and in tandem we've launched a co-ed flash poll right what we wanted to do was kind of twofold one really understand from CIOs the recovery type they had in mind as well as if they were seeing any any kind of permanent changes in their IT stacks IT spend because of koban 19 and so if we kind of look at the first chart here and kind of get more into that first question around recovery type what we asked CIOs and this kind of COBIT flash poll again we did it last Thursday was what type of recovery are you expecting is it v-shaped so kind of a brief decline you know maybe one quarter and then you're gonna start seeing growth in 2 to H 20 is it you shaped so two to three quarters of a decline or deceleration revenue and you're kind of forecasting that growth in revenue as an organization to come back in 2021 is it l-shaped right so maybe three four five quarters of a decline or deceleration and then you know very minimal to moderate growth or none of the above you know your organization is actually benefiting from from from koban 19 as you know we've seen some many reports so those are kind of the options that we gave CIOs and you kind of see it on that first chart here interesting and this is a survey a flash service 700 CIOs or approximately and the interesting thing I really want to point out here is this you know the koban pandemic was it didn't suppress you know all companies you know and in the return it's not going to be a rising tide lifts all ships you really got to do your research you have to understand the different sectors really try to peel back the onion skin and understand why there's certain momentum how certain organizations are accommodating the work from home we heard you know several weeks ago how there's a major change in in networking mindsets we're talking about how security is changing we're going to talk about some of the permanence but it's really really important to try to understand these different trends by different industries which you're going to talk about in a minute but if you take a look at this slide I mean obviously most people expect this u-shaped decline I mean a you know a u-shaped recovery rather so it's two or three quarters followed by some growth next year but as we'll see some of these industries are gonna really go deeper with an l-shape recovery and then it's really interesting that a pretty large and substantial portion see this as a tailwind presumably those with you know strong SAS models some annual recurring revenue models your thoughts if we kind of star on this kind of aggregate chart you know you're looking at about forty four percent of CIOs anticipated u-shaped recovery right that's the largest bucket and then you can see another 15 percent and to say an l-shape recovery 14 on the v-shaped and then 16 percent to your point that are kind of seeing this this tailwind but if we kind of focus on that largest bucket that you shaped you know one of the thing to remember and again when we asked is two CIOs within the within this kind of coded flash poll we also asked can you give us some commentary and so one of the things that or one of the themes that are kind of coming along with this u-shaped recovery is you know CIOs are cautiously optimistic about this u-shaped recovery you know they believe that they can get back on to a growth cycle into 2021 as long as there's a vaccine available we don't go into a second wave of lockdowns economic activity picks up a lot of the government actions you know become effective so there are some kind of let's call it qualifiers with this bucket of CIOs that are anticipating a u-shape recovery what they're saying is that look we are expecting these things to happen we're not expecting that our lock down we are expecting a vaccine and if that takes place then we do expect an uptick in growth or going back to kind of pre coded levels in in 2021 but you know I think it's fair to assume that if one or more of these are apps and and things do get worse as all these states are opening up maybe the recovery cycle gets pushed along so kind of at the aggregate this is where we are right now yeah so as I was saying and you really have to understand the different not only different sectors and all the different vendors but you got to look into the industries and then even within industries so if we pull up the next chart we have the industry to the breakdown and sort of the responses by the industries v-shape you shape or shape I had a conversation with a CIO of a major resort just the other day and even he was saying what was actually I'll tell you it was Windham Resorts public company I mean and obviously that business got a good crush they had their earnings call the other day they talked about how they cut their capex in half but the stock sagar since the March lows is more than doubled yeah and you know that's amazing and now but even there within that sector they're peeling that on you're saying well certain parts are going to come back sooner or certain parts are going to longer depending on you know what type of resort what type of hotel so it really is a complicated situation so take us through what you're seeing by industry sure so let's start with kind of the IT telco retail consumer space Dave to your point there's gonna be a tremendous amount of bifurcation within both of those verticals look if we start on the IT telco side you know you're seeing a very large bucket of individuals right over twenty percent that indicated they're seeing a tail with our additional revenue because of covin 19 and you know Dave we spoke about this all the way back in March right all these work from home vendors you know CIOs were doubling down on cloud and SAS and we've seen how some of these events have reported in April you know with this very good reports all the major cloud vendors right select security vendors and so that's why you're seeing on the kind of telco side definitely more positivity right as it relates to recovery type right some of them are not even going through recovery they're they're seeing an acceleration same thing on the retail consumer side you're seeing another large bucket of people who are indicating what we've benefited and again there's going to be a lot of bifurcation here there's been a lot of retail consumers you just mentioned with the hotel lines that are definitely hurting but you know if you have a good online presence as a retailer and you know you had essential goods or groceries you benefited and and those are the organizations that we're seeing you know really indicate that they saw an acceleration due to Koga 19 so I thought those two those two verticals between kind of the IT and retail side there was a big bucket or you know of people who indicated positivity so I thought that was kind of the first kind of you know I was talking about kind of peeling this onion back you know that was really interesting you know tech continues to power on and I think you know a lot of people try I think that somebody was saying that the record of the time in which we've developed a fit of vaccine previously was like mumps or something and it was I mean it was just like years but now today 2020 we've got a I we've got all this data you've got these great companies all working on this and so you know wow if we can compress that that's going to change the equation a couple other things sagar that jump out at me here in this chart I want to ask you about I mean the education you know colleges are really you know kind of freaking out right now some are coming back I know like for instance my daughter University Arizona they're coming back in the fall evidently others are saying and no you can clearly see the airlines and transportation as the biggest sort of l-shape which is the most negative I'm sure restaurants and hospitality are kind of similar and then you see energy you know which got crushed we had you know oil you know negative people paying it big barrels of oil but now look at that you know expectation of a pretty strong you know you shape recovery as people start driving again and the economy picks up so maybe you could give us some thoughts on on some of those sort of outliers yeah so I kind of bucket you know the the next two outliers as from an l-shaped in a u-shaped so on the l-shaped side like like you said education airlines transportation and probably to a little bit lesser extent industrials materials manufacturing services consulting these verticals are indicating the highest percentages from an l-shaped recovery right so three plus orders of revenue declines and deceleration followed by kind of you know minimal to moderate growth and look there's no surprise here those are the verticals that have been impacted the most by less demand from consumers and and businesses and then as you mentioned on the energy utility side and then I would probably bucket maybe healthcare Pharma those have some of the largest percentages of u-shaped recovery and it's funny like I read a lot of commentary from some of the energy in the healthcare CIOs and they were said they were very optimistic about a u-shaped type of recovery and so it kind of you know maybe with those two issues then you could even kind of lump them into you know probably to a lesser extent but you could probably open into the prior one with the airlines and the education and services consulting and IMM where you know these are definitely the verticals that are going to see the longest longest recoveries it's probably a little bit more uniform versus what we've kind of talked about a few minutes ago with you know IT and and retail consumer where it's definitely very bifurcated you know there's definitely winners and losers there yeah and again it's a very complicated situation a lot of people that I've talked to are saying look you know we really don't have a clear picture that's why all these companies have are not giving guidance many people however are optimistic not only for a vet a vaccine but but but also they're thinking as young people with disposable income they're gonna kind of say dorm damn the torpedoes I'm not really going to be exposed and you know they can come back much stronger you know there seems to be pent up demand for some of the things like elective surgery or even the weather is sort of more important health care needs so that obviously could be a snap back so you know obviously we're really closely looking at this one thing though is is certain is that people are expecting a permanent change and you've got data that really shows that on the on the next chart that's right so one of the one of the last questions that we asked on this you know quick coded flash poll was do you anticipate permanent changes to your kind of IT stack IT spend based on the last few months you know as everyone has been working remotely and you know rarely do you see results point this much in one direction but 92% of CIOs and and kind of IT you know high level ITN users indicated yes there are going to be permanent changes and you know one of the things we talked about in March and look we were really the first ones you know you know in our discussion where we were talking about work from home spend kind of negating or balancing out all these declines right we were saying look yes we are seeing a lot of budgets come down but surprisingly we're seeing 2030 percent of organizations accelerate spent and even the ones that are spending less they even then you know some of their some of their budgets are kind of being negated by this work from home spend right when you think about collaboration tool is an additional VPN and networking bandwidth in laptops and then security all that stuff CIOs now continue to spend on because what what CIO is now understand as productivity has remained at very high levels right in March CIOs were very with the catastrophe and productivity that has not come true so on the margin CIOs and organizations are probably much more positive on that front and so now because there is no vaccine where you know CIOs and just in general the population we don't know when one is coming and so remote work seems to be the new norm moving forward especially that productivity you know levels are are pretty good with people working from home so from that perspective everything that looked like it was maybe going to be temporary just for the next few months as people work from home that's how organizations are now moving forward well and we saw Twitter basically said we're gonna make work from home permanent that's probably cuz their CEO wants to you know live in Africa Google I think is going to the end of the year I think many companies are going to look at a hybrid and give employees a choice say look if you want to work from home and you can be productive you get your stuff done you know we're cool with that I think the other point is you know everybody talks about these digital transformations you know leading into Kovan and I got to tell you I think a lot of companies were sort of complacent they talked the talk but they weren't walking the walk meaning they really weren't becoming digital businesses they really weren't putting data at the core and I think now it's really becoming an imperative there's no question that that what we've been talking about and forecasting has been pulled forward and you you're either going to have to step up your digital game or you're going to be in big trouble and the other thing that's I'm really interested in is will companies sub optimize profitability in the near term in order to put better business resiliency in place and better flexibility will they make those investments and I think if they do you know longer term they're going to be in better shape you know if they don't they could maybe be okay in the near term but I'm gonna put a caution sign a little longer term no look I think everything that's been done in the last few months you know in terms of having those continuation plans because you know do two pandemics all that stuff that is now it look you got to have that in your playbook right and so to your point you know this is where CIOs are going and if you're not transforming yourself or you didn't or you know lesson learned because now you're probably having to move twice as fast to support all your employees so I think you know this pandemic really kind of sped up you know digital transformation initiatives which is why you know you're seeing some companies desks and cloud related companies with very good earnings reports that are guiding well and then you're seeing other companies that are pulling their guidance because of uncertainty but it's it's likely more on the side of they're just not seeing the same levels of spend because if they haven't oriented themselves on that digital transformation side so I think you know events like this they typically you know Showcase winners and losers then you know when when things are going well and you know everything is kind of going up well I think that - there's a big you know discussion around is the ESPY overvalued right now I won't make that call but I will say this then there's a lot of data out there there's data and earnings reports there's data about this pandemic which change continues to change maybe not so much daily but you're getting new information multiple times a week so you got to look to that data you got to make your call pick your spot so you talk about a stock pickers market I think it's very much true here there are some some gonna be really strong companies emerging out of this you know don't gamble but do your research and I think you'll you'll find some you know some Dems out there you know maybe Warren Buffett can't find them okay but the guys at Main Street I think you know the I am I'm optimistic I wonder how you feel about about the recovery I I think we may be tainted by tech you know I'm very much concerned about certain industries but I think the tech industry which is our business is gonna come out of this pretty strong yeah we look at the one thing we we should we should have stated this earlier the majority of organizations are not expecting a v-shaped recovery and yet I still think there's part of the consensus is expecting a v-shaped recovery you can see as we demonstrate in some of the earlier charts the you know almost the majority of organizations are expecting a u-shaped recovery and even then as we mentioned right that you shape there is some cautious up around there and I have it you probably have it where yes if everything goes well it looks like 2021 we can really get back on track but there's so much unknown and so yes that does give I think everyone pause when it comes from an investment perspective and even just bringing on technologies and into your organization right which ones are gonna work which ones are it so I'm definitely on the boat of this is a more u-shaped in a v-shaped recovery I think the data backs that up I think you know when it comes to cloud and SAS players those areas and I think you've seen this on the investment side a lot of money has come out of all these other sectors that we mentioned that are having these l-shaped recoveries a lot of it has gone into the tech space I imagine that will continue and so that might be kind of you know it's tough to sometimes balance what's going on on the investor in the stock market side with you know how organizations are recovering I think people are really looking out in two to three quarters and saying look you know to your point where you set up earlier is there a lot of that pent up demand are things gonna get right back to normal because I think you know a lot of people are anticipating that and if we don't see that I think you know the next time we do some of these kind of coded flash bolts you know I'm interested to see whether or not you know maybe towards the end of the summer these recovery cycles are actually longer because maybe we didn't see some of that stuff so there's still a lot of unknowns but what we do know right now is it's not a v-shaped recovery agree especially on the unknowns there's monetary policy there's fiscal policy there's an election coming up there's a third there's escalating tensions with China there's your thoughts on the efficacy of the vaccine what about therapeutics you know do people who have this yet immunity how many people actually have it what about testing so the point I'm making here is it's very very important that you update your forecast regularly that's why it's so great that I have this partnership with you guys because we you know you're constantly updating the numbers it's not just a one-shot deal so suck it you know thanks so much for coming on looking forward to having you on in in the coming weeks really appreciate it absolutely yeah well I will really start kind of digging into how a lot of these emerging technologies are faring because of kovat 19 so that's I'm actually interested to start thinking through the data myself so yeah well we'll do some reporting in the coming weeks about that as well well thanks everybody for watching this episode of the cube insights powered by ETR I'm Dave Volante for sauger kuraki check out ETR dot plus that's where all the ETR data lives i published weekly on wiki bon calm and silicon angle calm and reach me at evil on Tay we'll see you next time [Music]

>>LA Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and don't along with its ecosystem partners. >>Hey, welcome back to the cubes coverage of AWS reinvent 19 from Las Vegas. This is our third day of covering the event. Lots of conversations, two cube sets, as John would say, a Canon of cube content. Lisa Martin here with my esteemed colleague Justin. Um, and Justin, I have a couple of guests joining us. We've got to my left jus bhandarkar had a product for FreshWorks inc and Broughton Sahar VP and GM of machine learning services from Amazon. Gentlemen, welcome to the cube. You still have voices, which is very impressive after three days. A lot of practice it does or hiding out in quiet areas. Right? So Tay, Jess FreshWorks inc I souping on the website. Justin and I were talking before we went live, you guys have 150,000 of businesses using your technologies. I hadn't heard of FreshWorks, but it looks like it's about customer relationship management and customer experience. Tell our audience a little bit about what FreshWorks is and the technologies that you deliver. >>Okay. So we were founded in, uh, back in 2010. We were born in the cloud in the AWS cloud. Uh, and we started off as a, uh, customer support, uh, application. And we have grown on to now deliver a suite of customer engagement applications that include marketing and automation capabilities, CRM, uh, customer support and customer success. And so what we really are looking after is, is to deliver a value across the entire customer journey. Uh, you know, >>so there's been some big legacy CRMs around for a long time. What was the market opportunity back in 2010? The FreshWorks folks saw there's a gap here. We need to fill it. >>Yeah. We, well, we, uh, like any other startup, we decided to focus in one place and our focus, uh, was really around SMBs. We felt like SMBs were underserved and, uh, we felt like as rich as the technology is and the experiences have become, uh, we felt like we needed to democratize access to that. And because SMBs tended to have fewer resources and maybe, uh, in some cases weren't as tech savvy. Uh, we felt like they were kind of getting left behind. And so we wanted to step in there and make them whole and kind of offer them the same set of richness that you would expect, you know, for a large enterprise customer to have. And for that actually working in conjunction with AWS has been super important for us cause we have really been able to deliver on that promise. >>Maybe you can tell us a bit about the relationship between yourselves and FreshWorks. I believe the fresh works is built completely on AWS and always has been. >>Yeah. So how did that relationship begin and how has that grown as, as FreshWorks has grown into, into this massive company that you've become? >>Yeah, so, um, FreshWorks got off on AWS and then when we launched Sage maker and as you know, we have 700 tens of thousands of customers today doing the machine learning on AWS and on Sage maker. What customers have seen is that they get significant benefits in terms of features and developer productivity. And lower cost of ownership and FreshWorks saw that they could reduce that time to getting the models out by an order of magnitude. And their house was saying for example, that they used to take couple of days to get the models out to production. And by using Sage maker they were able to get it down to a couple of hours. And we have seen this happen with many other customers into it. For example, got down from six months to about a week. And just because of the productivity, performance and cost benefits that Sage makeup provides, you have seen the house FreshWorks and then many other companies, many of the customers more to AWS for the machine learning. >>Are they what are you using this machine learning to do? So you have all of these different models and we were talking a little bit before we went live about how you, how you use different models for different customers. But what are those models actually used to do? What service do they provide? >>Okay. So as you know, we have a set of these applications which are built around functional use cases. And so if you take a given customer, they might have multiple products from us and they might be doing multiple different use cases on us. And so you can quickly think of this as being, you know, maybe three to five specific use cases that require, you know, machine learning, you know, assistance. Uh, and so as a result, as we scale this up to the our entire, uh, set of customers, we now literally have thousands and thousands of these ML models that we have built, addressed, uh, geared to, uh, addressing specific pain points of that particular customer. Right? So it's all about catering the ML model for a specific use in a specific context. And then it's not only just about building it, uh, which, you know, obviously Sage Merker does a great job of helping us do that, but it's also about maintaining it over time and making sure that it stays relevant and fresh and so on. >>And again, working with AWS has been instrumental in for us to kind of stay ahead of that curve and make sure that we're continuing to drive accuracy and scale and simplicity into, uh, into, you know, into those particular use cases for customers. Then, you know, we released many features this year that makes this important. So one of the things that we have as part of Sage maker studio is a Sage make a model monitor that automatically monitors predictions and allows a customer to say, when are those predictions not being of the appropriate quality? And then we can send an allowance. So we are really building Sage maker out as a machine learning platform that they get all of the undifferentiated heavy lifting so that customers can really focus on what they need to do to build a model, train the model, and deploy the model. >>So in terms of your users, you mentioned too just the, the, the gap in the market back in 2010 was the small, the SMB space that probably something like a Salesforce or an Oracle was possibly too complex for an SMB. But now we're talking about emerging technologies, machine learning, AI. What is the appetite for the smaller, are you dealing with, I guess my question is a lot of SMBs that are born in the cloud companies, so smaller and more agile and more willing to understand and embrace technology versus legacy SMBs that might be, I don't want to say technology averse but not born within it. >>Yeah. So, so we, uh, we run through the entire gamut. So we obviously have, uh, you know, Silicon Valley based startups. We have more traditional companies around travel and hospitality and real estate and other, other verticals. Uh, and what we have really, really seen the commonality has been is that, uh, as good as the technology has become for AI and ML, uh, there is still some disparity in how people are able to consume it. Right. And if you have a lot of resources, a lot of skilled engineers, it is very easy for you to do that, thanks to all of the capabilities that are delivered by AWS. But in the other cases, uh, they do require more handholding specifically for those use cases that really impact them. Like how do I reduce my churn amongst customers? How do I maximize the chances of closing a deal? How do I make sure that the marketing campaign I run delivers on all of the, the objectives that I have? Right? So all of those things they re they need help. And so we are in there to kind of simplify that for them and leveraging all of the underlying technologies from AWS. We're able to deliver that together >>and going in from the beginning all in on AWS when AWS was only about four years old or so, right? Back in 2010. Um, talk to me about the opportunities that that is opened up for FreshWorks to evolve, you know, offer a suite of different solutions. Talk to us about Amazon and AWS is evolution and how quickly that they're evolving and developing new products and services as like fuel for FreshWorks business. >>Yeah. So really the big focus that we have always had is to deliver the right experiences that really impact end users. For those particular functional use cases around marketing, sales support and customer success, right? So as part of that, while we are focusing on on that experience, we also need to be focusing on delivering all of these services at scale, right? And with all the right security built in and all the right, uh, other, you know, tool set that that's built in. And so, so the synergy that we have found with between us and AWS is that we're able to rely on all of the right things for AWS to deliver upon. So they are also all about offering simple API APIs about making things scalable right from the get go about being extremely cost effective about uh, continuing to drive innovation. And these are all the things that drive us as well for our customers. And so it's been a very complimentary partnership from that respect is, you know, we kind of like go on this journey together and in our customer obsession is a key leadership principle. And so everything we do at AWS is really working back from the customer and making sure that we are really addressing all of the pain points. And making them successful? >>Well, because customer experience is a D it can be a deal breaker for companies, right? You think of you have a problem with your ISP and you call in or you go through social media or um, a chat bot and you can't get that problem resolved. As a consumer, you have so much choice to go to another vendor who might be able to better meet your needs or have the use the data to make sure they already know what's the problem. It's the same thing in the CRM space, right? If businesses don't have the right technologies to use the data to really know their customers, this customer's churn. And so it's really, we see CX as a driving force in any industry that if you can't get that right, customers are going to go, I'm going to go somewhere else because I have that choice. >>Yes. I mean customer expectations that you said have risen customer inpatients with bad experiences gone down. And one of the things that we have really focused on is as we go through this entire journey, we collect the data of that customer's journey. And we learn from it and we're able to visualize that for the sales person or the tech support person who's actually working with that customer. So they can actually see the journey of that customer. They visited the pricing page a couple of times, maybe they're interested to make a purchase or they visited the cancellation policy page. Okay, maybe I need to do something about that. Right. And so that is really been instrumental kind of in success success. And you know, what we are doing at AWS and Sage maker is making sure that all our customers get access to this technology. And that is where we start with how do we make machine learning accessible to all developers so that all of the experiences that we have gained at Amazon from investing in machine learning for the last 20 years, we take all of those learnings and make it available to our customers so they can apply machine learning for transforming their businesses. >>Yup. >>And that's exactly what it can be as transformational. Well gentlemen, thank you very much for joining Justin and me on the program talking to us about FreshWorks. What you guys are doing with Amazon and the opportunity to really dial up that CX experience with machine learning. We appreciate your time. >>Thank you. Thank you very much. >>All right. For my car is Justin Warren. I'm Lisa Martin and your Archie, the cube from AWS. Reinvent 19 from Vegas. Thanks.

[Narrator] Live from San Francisco it's the Cube covering Sparks Summit 2017 brought to you by databricks. >> Welcome to the Cube. My name is David Goad and I'm your host and we are here at Spark day two. It's the Spark Summit and I am flanked by a couple of consultants here from-- sorry, analysts from Wikibon. I got to get this straight. To my left we have Jim Kobielus who is our lead analysist for Data Science. Jim, welcome to the show. >> Thanks David. >> And we also have George Gilbert who is the lead analyst for Big Data and Analytics. I'll get this right eventually. So why don't we start with Jim. Jim just kicking off the show here today, we wanted to get some preliminary thoughts before we really jump into the rest of the day. What are the big themes that we're going to hear about? >> Yeah, today is the Enterprise day at Sparks Summit. So Spark for the Enterprise. Yesterday was focused on Spark, the evolution, extension of Spark to support for native development of deep learning as well as speeding up Spark to support sub-millisecond latencies. But today it's all about Spark and the Enterprise really what I call wrapping dev-ops around Spark, making it more productionizable, supportable. The databricks serverless announcement, though it was announced yesterday, the press release went up they're going into some depth right now in the key note about serverless and really serverless is all about providing an in cloud Spark, essentially a sand box for teams of developers to scale up and scale out enough resources to do the modeling, the training, the deployment, the iteration, the evaluation of Spark jobs in essentially a 24 by seven multi-tenant fully supported environment. So it's really about driving this continuous Spark development and iteration process into a 24 by seven model in the Enterprise, which is really what's happening is that data scientists, Spark developers are becoming an operational function that businesses are building, strategic infrastructure around things like recommendation engines, and e-commerce environments, absolutely demand 24 by seven resilience Spark team based collaboration environments, which is really what the serverless announcement is all about. >> David: So getting increasing demand on mission critical problems so that optimization is a big deal. >> Yeah, data science is not just an R&D function, it's an operational IT function as well. So that's what it's all about. >> David: Awesome, well let's go to George. I saw you watching the key note. I think still watching it again this morning, so taking notes feverishly. What were some of the things that stuck out to you from the key note speaker this morning? >> There are some things that are sort of going to bleed over from yesterday where we can explore some more. We're going to have on the show, the chief architect, Renald Chin, and the CEO, Ali Goatsee, and some of the things that we want to understand is how the scope of applications that are appropriate for Spark are expanding. We've got sort of unofficial guidance yesterday that, you know, just because Spark doesn't handle key value stores or databases all that tightly right now, that doesn't mean it won't in the future on the Apache Spark side through better APIs and on the databricks side, perhaps custom integration and the significance of that is that you can open up a whole class of operational apps, apps that run your business and that now incorporate, you know, rich analytics as well. Another thing that we'll want to be asking about is, keying off what Jim was saying, now that this becomes not a managed service where you just take the labor that the end customer was applying to get the thing running but it's now automated and you don't even know the infrastructure. We'll want to know what does that mean for the edge, you know, where we're doing analytics close to internet of things and people and sort of if there has to be a new configuration of Spark to work with that. And then of course what do we do about the whole data science process and the dev-ops for data science when you have machine learning distributed across the cloud and edge and On-Prem. >> Jim: In fact, I know we have Pepperdata coming on right after this, who might be able to talk about that exact dev-ops in terms of performance optimization into distributed Spark environment, yeah. >> George, I want to follow up with that. We had Matt Fryer from Hotels.com, he's going to be on our show later but he was on the key note stage this morning. He talked about going all cloud, all Spark, and how data science is even competitive advantage for Hotels.com. What do you want to dig into when we get him on the show? >> That's a really good question because if you look at business strategy, you don't really build a sustainable advantage just by doing one thing better than everyone else. That's easier to pick off. The sustainable strategic advantages come from not just doing one thing better than everyone else but many things and then orchestrating their improvement over time and I'd like to dig into how they're going to do that. 'Cause remember Hotels.com it's the internet equivalent descendant of the original travel reservation systems, which did confer competitive advantage on the early architects and deployers of that technology. >> Great and then Pepperdata wanted to come back and we're going to have them on the show here in just a moment. What would you like to learn from them? What do you think will benefit the community the most? >> Jim: Actually, keying off something George said, I'd like to get a sense for how you optimize Spark deployments in a radically distributed IOT edge environment. Whether they've got any plans, or what their thoughts are in terms of the challenges there. As more the intelligence gets pushed to the edge much of that will be on machine learning and deep learning models built into Spark. What are the challenges there? I mean, if you've got thousands to millions of end points that are all autonomious and intelligent and they're all running Spark, just what are the orchestration requirements, what are the resource management requirements, how do you monitor end-to-end in and environment like that and optimize the passing of data and the transfer of the control flow or orchestration across all those dispersed points. >> Okay, so 30 seconds now, why should the audience tune into our show today? What are they going to get? >> I think what they're going to get is a really good sense for how the emerging best practices for optimizing Spark in a distributed fog environment out to the edge where not just the edge devices but everything, all nodes, will incorporate machine learning and deep learning. They'll get a sense for what's been done today, what's the tooling is to enable dev-ops in that kind of environment. As well as, sort of the emerging best practices for compressing more of these algorithms and the data itself as well as doing training in a theoretically federated environment. I'm hoping to hear from some of the vendors who are on the show today. >> David: Fantastic and George, closing thoughts on the opening segment? 30 seconds. >> Closing thoughts on the opening segment. Like Jim is, we want to think about Spark holistically and it has traditionally been best position that's sort of this-- as Tay acknowledged yesterday sort of this offline branch of analytics that you apply to data like sort of repository that you accumulated and now we want to see it put into production but to do that you need more than just what Spark is today. You need basically a database or key value kind of option so that your storing your work as it goes along so you can go back and analyze it either simple analysis or complex analysis. So I want to hear about that. I want to hear about their plans for IOT. Spark is kind of a heavy weight environment, so you're probably not going to put it in the boot of your car or at least not likely anytime soon. >> Jim: Intelligent edge. I mean, Microsoft build a few weeks ago was really deep on intelligent edge. HP, who we're doing their show actually I think it's in Vegas, right? They're also big on intelligent edge. In fact, we had somebody on the show yesterday from HP going into some depth on that. I want to hear what databricks has to say on that theme. >> Yeah, and which part of the edge, is it the gateway, the edge gateway, which is really a slim down server, or the edge device, which could be a 32 bit meg RAM network card. >> Yeah. >> All right, well gentlemen appreciate the little insight here before we get started today and we're just getting started. Thank you both for being on the show and thank you for watching the Cube. We'll be back in a little while with our CEO from databricks. Thanks for watching. (upbeat music)

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